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Is AI splitting into two worlds?

Is AI splitting into two worlds?

Two developments recently have quietly revealed a deeper shift in AI.

One model exists behind closed doors, deployed to a small group tasked with securing critical systems. Another arrives openly, building software over hours-long sessions with no supervision.

Same field. Very different philosophies.

For AI professionals, this raises a more useful question than benchmarks or model size. What kind of ecosystem is emerging, and what does it mean for how we build, deploy, and trust AI?

The emergence of restricted frontier systems

Anthropic’s Project Glasswing introduces something unusual. A frontier model, Claude Mythos Preview, with strong gains across reasoning, coding, and vulnerability detection, yet deliberately kept out of public hands.

The reported capabilities stand out. Mythos identified thousands of security flaws across operating systems and browsers, including issues that survived decades of testing and millions of scans. 

That level of signal points to advances in long-context reasoning, codebase navigation, and multi-step inference.

More interesting than the performance is the deployment model.

💡
Access sits with a small coalition of partners. The goal focuses on defensive cybersecurity, with structured rollout and controlled environments. This marks a shift from “release and iterate” toward something closer to “contain and validate.”

For practitioners, this introduces a new category of model:

  • Systems operating in controlled, high-trust environments
  • Capabilities withheld by design rather than by limitation
  • Deployment shaped by risk surface instead of user demand

This reframes how frontier capability enters the ecosystem. Instead of broad release followed by patchwork mitigation, capability arrives paired with governance from day one.

There is also a quieter technical signal. Models at this level appear to exhibit behaviors that extend beyond predictable task execution. Reports of unexpected actions during internal testing hint at systems that require tighter boundaries, stronger observability, and more deliberate constraint design.

In other words, the model stops being just a tool and starts behaving more like a system you need to manage carefully. Slightly less “run this prompt” and slightly more “monitor this process.”

The machine learning paradox: Can AI actually learn?
We call it machine learning. But do machines actually learn? Today’s AI systems train, optimize, and scale, but real learning is something else entirely. The distinction matters more than the industry wants to admit.
Is AI splitting into two worlds?AI Accelerator InstituteAndrew Lovell
Is AI splitting into two worlds?

The acceleration of open capability

In parallel, Zhipu AI’s GLM-5.1 takes a very different path.

💡
An open-source model reaching the top of SWE-Bench Pro marks a meaningful moment. Coding benchmarks serve as a proxy for structured reasoning, tool use, and multi-step execution. Leading that benchmark suggests open models are advancing along dimensions once dominated by closed systems.

The more interesting signal lies in long-horizon execution. Demonstrations of multi-hour autonomous sessions suggest improvements in memory persistence, task decomposition, and iterative refinement. These are core ingredients for agentic workflows.

From a systems perspective, this reflects progress in areas such as:

  • Persistent context across extended execution cycles
  • Stable tool use over multiple iterations
  • Sustained output quality over time

For developers, this opens a different layer of experimentation. Instead of prompting for outputs, teams can design workflows where models plan, execute, and adapt over extended periods.

Open access amplifies this effect. It allows teams to:

  • Inspect behavior under real workloads
  • Fine-tune models for domain-specific tasks
  • Integrate deeply into internal systems

This creates a feedback loop where capability and adoption reinforce each other. More usage leads to better patterns, better tooling, and faster iteration.

Also, a small but important detail. When a model can run for eight hours straight building something useful, it quietly changes expectations. The question shifts from “Can it help?” to “How much can it take off my plate today?”

Two trajectories, one ecosystem

Together, these developments point to a clear split.

On one side, highly capable models operate within restricted environments, optimized for safety, reliability, and controlled deployment. On the other hand, increasingly capable open models enable broad experimentation and rapid iteration.

This introduces a set of tensions that extend beyond performance:

  • Access vs control. Restricted models concentrate capability within a small group. Open models distribute it widely.
  • Safety vs speed. Controlled deployments emphasize risk mitigation. Open ecosystems move quickly through experimentation.
  • Reliability vs flexibility. Closed systems offer tighter guarantees. Open systems offer adaptability and customization.

For AI professionals, this shapes architectural decisions. Model choice becomes less about raw capability and more about alignment with system requirements, risk tolerance, and operational constraints.

The story of Sora: What it reveals about building real-world AI
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Is AI splitting into two worlds?AI Accelerator InstituteAndrew Lovell
Is AI splitting into two worlds?

Implications for building agentic systems

The rise of agentic AI adds another layer to this divide.

As systems shift from assistive to execution-oriented, evaluation shifts toward outcomes. Task completion, quality, and time to fulfillment move to the center.

In this context, model characteristics matter differently.

Restricted frontier models may offer:

  • Higher consistency in complex reasoning
  • Strong performance on edge cases
  • Safeguards aligned with enterprise requirements

Open models may offer:

  • Greater control over system design
  • Flexibility across domains
  • Faster iteration cycles

The choice influences system design end-to-end, from orchestration layers to monitoring and evaluation.

There is also a cultural difference. Teams working with open models tend to iterate quickly and learn from deployment. Teams working with restricted models often emphasize validation, compliance, and structured rollout.

Both approaches create value. The interesting question is how they begin to overlap.

So, who decides how advanced AI capability is accessed and applied?

If the most powerful systems remain restricted, a small number of organizations shape the boundaries of what gets built. If open models continue to close the gap, capability spreads more widely, along with the responsibility that comes with it.

For the industry, this creates parallel tracks of innovation with different incentives and timelines.

For practitioners, it introduces a strategic layer to system design. Model selection becomes part of a broader decision around governance, reliability, and long-term scalability.

💡
One thing feels clear: The conversation has moved beyond which model tops a benchmark. The more interesting discussion centers on how capability is deployed, who can access it, and how it shapes the systems being built.

And perhaps the most interesting signal sits just out of view.

The models everyone talks about are usually available. The ones that quietly reshape workflows tend to sit behind the scenes, solving problems before anyone notices.

Which, for an industry that loves benchmarks, feels like a slightly ironic place to end up.

AI News

AI doesn’t actually learn. Here’s the problem

AI doesn’t actually learn. Here’s the problem

For something called machine learning, we spend surprisingly little time asking whether machines actually learn.

That isn’t a throwaway line. It’s the central tension behind a recent paper, and it cuts closer to home than much of the field might like.

Because if you look carefully, today’s AI systems do not learn in the way we intuitively mean. They train. They optimize. They scale.

Learning, however, sits in a different category…

The training illusion

Modern AI systems excel at one thing: extracting patterns from large datasets.

Give a model enough data, compute, and time, and it will:

  • Predict the next token
  • Classify images
  • Generate code
  • Pass exams that most of us would prefer to avoid

This represents real progress. It underpins most of the breakthroughs of the last decade.

There is, however, a structural catch.

💡
These systems learn almost entirely in a predefined training phase, often on carefully curated datasets. Once deployed, they operate largely as fixed functions. Updates require retraining, fine-tuning, or human intervention.

In other words, they operate without ongoing learning.

The paper frames this as a fundamental limitation: current systems lack autonomous, continuous learning, the ability to adapt dynamically in open environments.

This gap marks the difference between a system that performs well in benchmarks and one that behaves robustly in the real world.

The story of Sora: What it reveals about building real-world AI
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AI doesn’t actually learn. Here’s the problemAI Accelerator InstituteAndrew Lovell
AI doesn’t actually learn. Here’s the problem

Why this matters for AI professionals

At first glance, this might feel like academic hair-splitting. After all, if the model works, does the learning process matter?

In practice, it matters quite a lot.

The cracks show up in familiar places:

  • Systems struggle with out-of-distribution scenarios
  • Long-horizon tasks break down after a few steps
  • Context fades faster than anyone would like
  • Real-world interaction remains brittle

These patterns appear consistently, and they reflect structural consequences of how learning is currently defined.

The paper compares today’s paradigm to an assembly line: data is collected, models are trained, and outputs are produced. The system itself does not evolve through experience.

That approach works well for static tasks. It performs less effectively in a dynamic world.

Learning, according to biology

To understand what’s missing, the authors take an unusual route for an AI paper and look to cognitive science.

Biological systems do not separate learning into neat phases. They combine multiple modes of learning, continuously:

  • Passive observation
  • Active interaction
  • Internal control over what to learn and when

The paper formalizes this into three components:

1. System A: Learning from observation

This aligns closely with what current models do: learning patterns from data, often through self-supervision.

2. System B: Learning from action

Here, learning happens through interaction with the environment, through trial, error, feedback, and adaptation.

3. System M: Meta-control

This is the interesting layer. A system that decides how to learn, when to observe, when to act, and how to allocate resources.

Current AI systems lean heavily toward System A. The other two appear only in partial form.

Which explains quite a lot.

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AI doesn’t actually learn. Here’s the problemAI Accelerator InstituteAndrew Lovell
AI doesn’t actually learn. Here’s the problem

The missing ingredient: Autonomy

The paper’s core claim is simple and slightly uncomfortable:

Today’s AI systems function as non-autonomous learners.

They rely on:

  • Curated datasets
  • Predefined objectives
  • External supervision

They lack the ability to:

  • Generate their own learning signals
  • Adapt continuously to new environments
  • Build internal models that evolve over time
💡
This explains why scaling alone shows diminishing returns. Performance improves, yet the learning paradigm stays the same. It resembles upgrading a car’s engine while leaving the steering system untouched.

What a different architecture might look like

The authors move beyond critique and outline a path forward, blending ideas from reinforcement learning, self-supervision, and cognitive science.

At a high level, future systems would:

  • Learn from both observation and interaction, rather than static data alone
  • Continuously update their internal representations
  • Use meta-control mechanisms to guide learning dynamically
  • Operate in open-ended environments instead of fixed datasets

In this framing, learning becomes an ongoing process rather than a one-time event.

Training becomes the starting point.

Two shifts this implies

If you take the paper seriously, it points to two broader shifts for the field:

1. From datasets to environments

The center of gravity moves away from static corpora toward interactive, evolving environments.

Think less “pre-training on the internet,” more “learning through experience.”

2. From optimization to adaptation

Performance metrics shift from accuracy on benchmarks to adaptability over time.

The question changes from “how well does it perform?” to “how quickly can it improve?”

A quick reality check

Before declaring a major shift in machine learning, it helps to stay grounded.

Current approaches:

  • Work extremely well in many domains
  • Scale predictably
  • Deliver real economic value

All of that remains true.

What this paper highlights is not failure, but incompleteness.

The field has built systems that excel at extracting patterns from data. Systems that interact with data continuously, adapt in real time, and evolve through experience still sit on the horizon.

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AI doesn’t actually learn. Here’s the problemAI Accelerator InstituteValentin Marenich
AI doesn’t actually learn. Here’s the problem

So… does AI learn?

Yes, within a narrow and well-defined frame.

It learns during training. It generalizes within bounds. It performs impressively under the right conditions.

Continuous, interactive, self-directed learning remains out of reach.

That distinction matters.

Because the next wave of AI progress likely depends less on scaling the same paradigm and more on expanding what “learning” actually means.

Final thought

The field has achieved remarkable progress through pattern recognition.

Now it’s encountering the limits of that success.

The question has shifted.

It’s no longer about whether machines can learn from data. It’s now about whether they can learn from the world.

And that is a much harder problem, though it does come with better benchmarks than ImageNet.

AI News

Behind the Blog: Smoking the Whole Carton


Behind the Blog: Smoking the Whole Carton

This is Behind the Blog, where we share our behind-the-scenes thoughts about how a few of our top stories of the week came together. This week, we discuss gun violence and chatbots and acceptance of depravity.

EMANUEL: It takes a lot for a post to shock me these days, especially if it’s from a known shitposter like the president of the United States, but I’ll confess that I was shocked by Trump posting a video of a woman getting beaten to death with a hammer last night. 

I’m not a big believer in the “Trump is doing X because he doesn’t want you to think about Y” theory, but it’s hard not to read as at least an intuitive desire to change the subject away from the conclusion (?) to his disastrous adventure in Iran. It’s not going to work either way, and that’s not going to be the legacy of his posting style either. What’s sticking with me at the moment is not the graphic nature of the post itself or the attempt to demonize certain minorities—unfortunately none of that is surprising at this point—but that I don’t know if it’s possible to roll back this level of acceptance of even embrace of depravity in our culture. 

Remote Jobs

Flag Theory: Multi-Jurisdiction Accountant for an International Client Portfolio (Europe)

Headquarters: Remote

URL: https://flagtheory.com/

Remote in Europe I Contractor I Full Time

We are seeking an experienced Multi-jurisdiction accounting professional to oversee accounting operations and financial reporting for a portfolio of client entities while managing a small accounting team. The successful candidate will be responsible for ensuring accurate financial reporting, regulatory compliance and efficient workflow allocation.

 

Accounting Oversight & Team Management

  • Manage and supervise a small team of accounting professionals, including allocation and review of work assignments

  • Preparation of financial statements for client entities

  • Ensure accounting entries, allocations, and supporting documentation are accurate and properly maintained

  • Maintain organised accounting records and documentation for audit and regulatory review

 

Financial Reporting & Client Deliverables

  • Prepare and review periodic clients’ financial statements, balance sheets, cash flow reports, and management accounts

  • Support month-end and year-end closing processes

  • Deliver timely financial reporting to clients

 

Tax & Regulatory Coordination

  • Prepare or oversee preparation of client tax returns and regulatory reporting

  • Coordinate with external tax advisors, auditors, and regulatory professionals

  • Ensure compliance with applicable accounting and reporting standards

 

Qualifications & Experience

  • Proven experience in an accounting or finance management role, preferably within a professional services or multi-entity environment

  • Minimum 5 years of combined accounting, bookkeeping, and financial reporting experience, including supervision or review responsibilities

  • Demonstrated experience preparing and reviewing financial statements and coordinating tax reporting obligations

  • Experience managing accounting matters involving multi-jurisdictional or cross-border corporate structures

  • Strong organisational, prioritisation, and team coordination skills, with the ability to allocate and review work effectively

  • Experience in a client-facing finance or accounting role, managing multiple stakeholders and deadlines

  • Thorough knowledge of accounting principles, financial reporting standards, and accounting procedures

  • Practical experience with general ledger management and month-end and year-end close processes

  • Advanced proficiency in spreadsheet modelling and financial systems

  • Strong working knowledge and administrative experience with accounting software (e.g. Xero or similar platforms)

 

Working Environment & Requirements

  • Availability overlapping with Asian afternoon and European business hours

  • Ideally located within the GMT+4 to GMT  time zone range

  • Ability to operate effectively in a low-ego, highly collaborative, and cross-functional environment, while maintaining a high degree of autonomy

  • Adaptability to internal workflows, methodologies, and operational processes

  • Reliable internet connection and professional, distraction-free remote working environment (home office or co-working space)

  • Fluent written and spoken English

Nice to have

  • Prior experience working in a fully remote or distributed team environment

  • Familiarity with digital assets, blockchain, or cryptocurrency-related businesses

  • Experience working with technology-driven, startup, or high-growth organisations

 

What we offer

  • Flexible, remote work environment

  • Long-term commitment and professional growth opportunities

  • Compensation based on experience

  • 20 paid vacation days per year

 

Location:

  • Remote in Europe, Contractor, Full time

Please apply using this link: https://forms.gle/fJkQLcGqiNne6drB8

 

To apply: https://weworkremotely.com/remote-jobs/flag-theory-multi-jurisdiction-accountant-for-an-international-client-portfolio-europe

Remote Jobs

Lemon.io: Senior AI Engineer / Data Annotator

Headquarters: New York, NY

URL: https://lemon.io

Are you a talented Senior Engineer looking for a remote job that lets you show your skills and get decent compensation? Look no further than Lemon.io — the marketplace that connects you with hand-picked startups in the US and Europe.

What we offer:

  • The rate depends on your skills and experience. We’ve already paid out over $11M to our engineers.
  • No more hunting for clients or negotiating rates — let us handle the business side of things so you can focus on what you do best.
  • We’ll manually find the best project for you according to your skills and preferences.
  • Choose a schedule that works best for you. It’s possible to communicate async or minimally overlap within team working hours.
  • We respect your seniority so you can expect no micromanagement or screen trackers.
  • Communicate directly with the clients. Most of them have technical backgrounds. Sounds good, yeah?
  • We will support you from the time you submit the application throughout all cooperation stages.
  • Most of our projects involve working in a fast-paced startup environment. We hope you like it as much as we do.
  • Through our community, we will connect you with the best developers from more than 71 countries.

We are seeking specialists in AI/Data: AI Engineers, Data Annotators, Data Engineers, MLOps Engineers, Data Analysts, and Data Scientists. Below are the requirements for some of the roles:

Requirements – AI Engineers:

  • 3+ years of commercial experience applying AI to practical technology solutions;
  • 3+ years of experience with Python required; experience with other programming languages commonly used in AI is a plus;
  • At least 3 years of experience with LLMs and 2+ years of experience in AI agent development, LangChain, or OpenAI required, OR 2+ years of expertise with LangChain and OpenAI;
  • Familiarity with AWS, GCP, or Azure required;
  • Experience developing and shipping AI agents.

Requirements – Data Annotator:

  • 4+ years of commercial experience as an engineer;

  • 3+ years of commercial experience with Python.

  • Strong technical skills: as a Senior Engineer, you are expected to be able to create projects from scratch and have a deep understanding of application architecture.

  • Clear and effective communication in English — advanced ability to discuss business tasks, justify decisions, and communicate issues. Good self-presentation is also essential for upcoming client calls.

  • Strong self-organizational skills — ability to work full-time remotely with no supervision.

  • Reliability — we want to trust you and expect that you won’t let us and the client down.

  • Adaptability and Flexibility — the ability to onboard the project promptly after accepting it and start delivering results quickly.

Sounds good for you? Apply now and join the Lemon.io community!

NOT YOUR TECH STACK?

We have multiple projects available for Senior Developers. If you have 4+ years of commercial software development experience and are proficient in any of the following areas: React & Python, React & Node, React & Ruby, PHP & Angular, PHP & Vue, Vue & Node.js, React & .NET, Android & iOS, Angular & .NET, Angular & Node.js, Vue & .NET, Python & Vue, MLOps, React & Java, Data Science, Blockchain (Web3/Solidity/Solana), Symfony & React, Symfony & Vue, Symfony & Angular, Symfony & JavaScript & Next.js & TypeScript, Data Analysis, React & PHP, Data Engineering, DevOps, React & Golang, Svelte & Python, Svelte & Node, Svelte & TypeScript, Rust, Golang, Shopify & JavaScript, Vue & Nuxt, Python & Node, Angular & TypeScript, Ruby & Ruby on Rails, React Native & Ruby, React Native & Python, PHP & Laravel, .NET & C#, Java & Spring, Unreal Engine & C++, Python & LLM, Unity, Machine Learning Engineering, Python & Flask — we’d be happy to connect and match you with a suitable project.

If your experience matches our requirements, be ready for the next steps:

  • VideoAsk — watch a short video about our startup, up to 10 minutes
  • Complete your profile on our website
  • 30-minute screening call
  • Technical interview
  • Feedback
  • Magic Box (we are looking for the best project for you).

We do not provide visa assistance, and our cooperation model does not include the benefits typically offered with direct hire.

P.S. We work with developers from 71+ countries in different regions: Europe, LATAM, the U.S (if you are an owner of W-9 ben form), Canada, Asia (Japan, Singapore, South Korea, Philippines, Indonesia), Oceania (Australia, New Zealand, Papua New Guinea), and the the UK. However, we have some exceptions.

At the moment, we don’t have a legal basis to accept applicants from the following countries:

  • European: Hungary, Iceland, Liechtenstein, Kosovo, Belarus, Russia, and Serbia.
  • Latin America: Cuba and Nicaragua
  • Most Asian countries and Africa.

We expand and shorten the list of exemptions regularly.

Do you represent a company with engineers who match the description and want to collaborate with us through staff augmentation? Then register here.

 

To apply: https://weworkremotely.com/remote-jobs/lemon-io-senior-ai-engineer-data-annotator

Remote Jobs

WALTER: Supply Chain Manager (LATAM)

Headquarters: Remote

URL: http://gowalter.co

Supply Chain Manager (LATAM)

About the Role

We are partnering with a fast-growing company that is looking to bring a Supply Chain Manager to own and optimize the end-to-end supply chain across multiple eCommerce channels. This role is critical in ensuring product availability, cost efficiency, and smooth operations from factory to final delivery.

You’ll work closely with suppliers, freight partners, and internal teams to build a reliable, scalable supply chain that supports growth across platforms like Amazon, TikTok Shop, Walmart, and 3PLs.

 

What You’ll Do

Supplier & Production Management

  • Manage relationships with suppliers and factories for finished goods, packaging, and raw materials
  • Place and manage purchase orders to ensure optimal inventory levels across all channels
  • Negotiate pricing, terms, and lead times to improve cost of goods and cash flow

Inventory & Fulfillment

  • Oversee inventory planning across Amazon FBA, TikTok Shop (FBT), Walmart WFS, and 3PLs
  • Prevent stockouts and overstock through effective forecasting and replenishment
  • Support SKU launches, packaging updates, and product transitions

Logistics & Operations

  • Manage freight partners to ensure timely delivery from factories to warehouses
  • Monitor and resolve shipping delays, inventory discrepancies, and fulfillment issues
  • Ensure smooth coordination across international shipping, customs, and inspections

Cost Optimization & Performance

  • Optimize supply chain efficiency across shipping, warehousing, and fulfillment costs
  • Track and improve margins through better operational decisions
  • Build processes and systems that scale with business growth

Requirements

What We’re Looking For

Must-Have

  • Proven experience managing supply chain operations in eCommerce or CPG
  • Experience with Amazon FBA, TikTok Shop, Walmart WFS, and/or 3PLs
  • Strong supplier management and negotiation skills
  • Experience with international shipping and logistics
  • Ability to manage multiple moving parts with strong attention to detail

➕ Nice to Have

  • Experience in demand planning, forecasting, or S&OP
  • Background in supply chain analytics or data-driven decision making
  • Experience scaling operations across multiple sales channels

How You Work

  • Strong ownership mindset
  • Highly organized and execution-focused
  • Problem-solver who adapts quickly to changing conditions
  • Clear communicator with internal teams and external partners
  • Systems thinker — you build structure, processes, and scalable solutions

Benefits

Why Join

  •  100% Remote – work from anywhere. 
  •  Flexible schedule with focus on results. 
  •  Opportunity to work closely with a growing brand in the beauty & wellness industry. 
  •  Collaborative, creative, and fast-paced work environment. 
  •  Direct impact on the company’s growth and visibility on one of the most influential platforms today.
  •  

This is a unique opportunity to be at the intersection of e-commerce and social commerce, shaping how a brand shows up on the most influential platforms today. You’ll have space to experiment, bring your ideas to life, and directly impact growth.

 

To apply: https://weworkremotely.com/remote-jobs/walter-supply-chain-manager-latam

Remote Jobs

Everis: Marketing – Growth, Content & Acquisition

Headquarters: Las Vegas, Nevada

URL: https://everis.ai/

100% remote. No direct marketing job experience necessary. We hire based on demonstrated results, not resumes. If you’ve built an audience, grown a channel, launched campaigns that worked, or created content that moved the needle – even on your own projects – we want to hear from you.

Why this is the best job on the planet for a marketer with skill and ambition:

  • Great starting salary.

  • You will work on real products with real customers, and you’ll see your marketing efforts directly impact traffic, signups, revenue, and brand awareness.

  • You don’t need a marketing degree or years of agency experience. What matters is that you’ve already proven you can get results through your own initiative – whether that’s growing a social media following, running ads that converted, writing content that ranked, building an email list, or anything else that moved real numbers.

  • You will have an opportunity to get equity in the business.

  • You’ll help take recently acquired apps and businesses from unknown to well-known, driving growth across multiple products simultaneously.

  • This is an environment where you will be stretched and learn faster than pretty much any other role because it’s very hands-on and practical with the ability to take on responsibilities fast.

What you’ll work on

Really, it’s up to you what areas of marketing you focus on. You can focus on your own strengths as long as you’re delivering results. You don’t have to be across all areas of marketing, and you can specialize in what you do best. Having said that, here are some potential areas that you can get involved with:

  • Planning and executing marketing campaigns across channels – social media, content, email, paid ads, SEO, partnerships, or whatever works best for each product.

  • Creating compelling content (blog posts, social content, landing pages, email sequences, video scripts) that drives traffic, engagement, and conversions.

  • Analyzing what’s working and what’s not, then doubling down or pivoting quickly. You’ll own the metrics and be expected to move them.

  • Building and growing audiences across platforms – identifying where our customers spend their time and meeting them there.

  • Working directly with founders and operators on go-to-market strategy, positioning, and user acquisition for newly acquired products.

  • Experimenting constantly – testing new channels, messages, offers, and creative approaches to find what drives growth for each product in our portfolio.

About Everis

We are a new business started by successful technology founders that is focused on buying small internet businesses and apps and operating them for the long term. We will be improving the business meaningfully, and you’ll be part of that process of improving the business and making it more useful and valuable.

What we offer

  • Work with talented entrepreneurial people.

  • You’ll have a bigger opportunity to grow faster than in any other business pretty much because there are no limits in terms of age or experience. We only look at your contribution to the business.

  • Make your own decisions, act as an entrepreneur within the business.

  • It’s ok to fail as long as you are failing fast.

  • Work with flexible hours and location, and meet with us 1-2 times per year in a beautiful location around the world.

  • Starting salary for this role is $60-100,000 USD per year depending on background and demonstrated results.

  • After 1 year of working remotely, high performing team members are eligible for sponsorship to live and work in Dubai or can continue to work where they are remotely.

  • Opportunity to receive some of your pay in equity at a discounted price.

What’s the downside?

  • We will have a lot of applicants, and most will not make it past the first round.

  • The first 90 days are very demanding. If you need structure, hand-holding, or certainty, this will be a bad fit.

  • You will be expected to figure things out on your own and take responsibility quickly.

  • You’ll be working in a fast-moving environment where priorities change based on what the data is telling us and what the business needs most.

Excited to work with you and build this into a historic company!

To apply: https://weworkremotely.com/remote-jobs/everis-marketing-growth-content-acquisition

Remote Jobs

NoGigiddy: Remote Data Entry Clerk

Headquarters: Atlanta, Georgia

URL: https://www.nogigiddy.com/

Job Summary:
We are looking for a Remote Data Entry Clerk to join our team and help us maintain accurate and up-to-date information in our databases and systems. The ideal candidate will have excellent typing skills, an eye for detail, and the ability to work independently. This role is crucial to ensuring that our data is reliable and easily accessible to our team members and clients.
Key Responsibilities:
 • Accurately enter data into various databases and systems from source documents within time limits
 • Review data for deficiencies or errors, correct any incompatibilities, and check the output
 • Verify data by comparing it to source documents
 • Update existing data and retrieve data from the database as requested
 • Perform regular backups to ensure data preservation
 • Organize and maintain files and records for efficient data retrieval
 • Collaborate with team members to address any discrepancies or issues with data entry
 • Maintain confidentiality and security of sensitive information
Requirements:
 • Proven experience as a Data Entry Clerk or similar role
 • Excellent typing speed and accuracy
 • Strong attention to detail and ability to spot errors
 • Proficiency in using data entry software and Microsoft Office Suite (Word, Excel, etc.)
 • Ability to work independently and meet deadlines
 • Strong organizational and time management skills
 • Excellent communication skills, both written and verbal
 • High school diploma or equivalent; additional qualifications in data management or related fields are a plus
Preferred Qualifications:
 • Experience with remote work and virtual collaboration tools
 • Familiarity with data protection regulations and best practices
Compensation:
 • Competitive pay rate of $18 to $24 per hour
 • Flexible working hours and remote work environment
 • Opportunities for professional growth and development
 • Supportive and collaborative team culture
 • Access to the latest technology and tools to perform your job efficiently

To apply: https://weworkremotely.com/remote-jobs/nogigiddy-remote-data-entry-clerk-7

Remote Jobs

Rubrik: Sales Engineering Manager, Cloud Product Line Specialists

Headquarters: United States

About Team & About Role:

Rubrik’s sales organization is a united group of elite cross-functional sales professionals that help companies & government entities achieve resilience against cyberattacks, malicious insiders, and operational disruptions. We offer continuous professional development through our world class sales enablement program and our One Rubrik selling approach provides all the resources you need to exceed your goals, maximize your earnings potential and take your career to the next level. All this while doing something that truly matters, protecting the world’s data.  

Rubrik is looking for a Sales Engineering Manager to lead a team of Cloud Specialist Sales Engineers and provide technical direction and business guidance to the regional sales team. You will be accountable for regional revenue goals, recruiting and hiring top talent, enabling Sales Engineers to be best in business and by driving innovative technical programs and overseeing day-to-day account-level activities. You will be responsible for evangelizing, positioning, and architecting the industry’s first hyper-converged hybrid cloud data management platform for both existing customers and new accounts. 

What You’ll Do:

  • Leads a team of Sales Engineers across the Americas region

  • Responsible for recruiting, hiring and enabling top SE’s as we expand our coverage

  • Provide Technical Leadership and direction to the Sales Teams in the Region to help them build technology solutions for our customers and prospective customers.

  • Assists in the analysis, design and development of fully integrated technology solutions.

  • Demonstrates technical leadership and subject matter expertise on Rubrik’s products, distributed architectures, file systems, and competitive storage offerings in the Cloud and NAS product space.

  • Acts as technical expert

  • Makes technical and sales presentations to customer’s technical staff and senior management.

  • Serves as a trusted technology advisor to customers and serves as an internal resource on technical issues or specific business applications within an assigned market segment.

  • Successfully builds relationships with the account team, partners and customers in support of sales team objectives and engages and leverages corporate resources, abilities, budgets and personnel as appropriate.

Experience You’ll Need:

  • 3-5 years of sales engineering management experience preferably in a Cloud Infrastructure environments, working with AWS, Azure and GCP

  • Driven – need for success, highly energetic with a strong hands-on, “can do” approach.

  • The successful candidate must have a fundamental breadth of technical knowledge in cloud data management, backup and disaster recovery and data analytics.

  • Entrepreneurial – willing to go the extra mile, strong work ethic, resourceful, “get it done” attitude

  • Strives in moving in a fast-paced environment; including handling multiple calls/demos per day with immediate follow up.

  • A high level of business acumen and experience working with Cx0 level personnel, bringing technology solutions to solve business challenges.

  • Smart, adaptable and open-minded

  • Bachelor’s degree required or equivalent experience

#LI-DNI

#LI-REMOTE

The minimum and maximum base salaries for this role are posted below; this role is also eligible to earn commissions pursuant to the Company’s written Incentive Compensation Plan. Additionally, the role is eligible for equity and benefits. The range displayed reflects the minimum and maximum target for new hire salaries for the role based on U.S. location. Within the range, the salary offered will be determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

US Pay Range
$135,600—$216,600 USD

Join Us in Securing the World’s Data

Rubrik (RBRK), the Security and AI Operations Company, leads at the intersection of data protection, cyber resilience, and enterprise AI acceleration. Rubrik Security Cloud delivers complete cyber resilience by securing, monitoring, and recovering data, identities, and workloads across clouds. Rubrik Agent Cloud accelerates trusted AI agent deployments at scale by monitoring and auditing agentic actions, enforcing real-time guardrails, fine-tuning for accuracy and undoing agentic mistakes. 

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Inclusion @ Rubrik

At Rubrik, we are dedicated to fostering a culture where people from all backgrounds are valued, feel they belong, and believe they can succeed. Our commitment to inclusion is at the heart of our mission to secure the world’s data.

Our goal is to hire and promote the best talent, regardless of background. We continually review our hiring practices to ensure fairness and strive to create an environment where every employee has equal access to opportunities for growth and excellence. We believe in empowering everyone to bring their authentic selves to work and achieve their fullest potential.

Our inclusion strategy focuses on three core areas of our business and culture:

  • Our Company: We are committed to building a merit-based organization that offers equal access to growth and success for all employees globally. Your potential is limitless here.

  • Our Culture: We strive to create an inclusive atmosphere where individuals from all backgrounds feel a strong sense of belonging, can thrive, and do their best work. Your contributions help us innovate and break boundaries.

  • Our Communities: We are dedicated to expanding our engagement with the communities we operate in, creating opportunities for underrepresented talent and driving greater innovation for our clients. Your impact extends beyond Rubrik, contributing to safer and stronger communities.

Equal Opportunity Employer/Veterans/Disabled

Rubrik is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.

Rubrik provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, Rubrik complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. 

Federal law requires employers to provide reasonable accommodation to qualified individuals with disabilities. Please contact us at hr@rubrik.com if you require a reasonable accommodation to apply for a job or to perform your job. Examples of reasonable accommodation include making a change to the application process or work procedures, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.

EEO IS THE LAW

NOTIFICATION OF EMPLOYEE RIGHTS UNDER FEDERAL LABOR LAWS

To apply: https://weworkremotely.com/remote-jobs/rubrik-sales-engineering-manager-cloud-product-line-specialists