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About This Role
Epoch AI is looking for an AI Enablement Manager to help our team better utilize AI in our workflows. You would build and run automations that take routine work off people’s plates, keep our shared automations running smoothly, and help researchers and operations staff use frontier models better in their work.
About the Role
Epoch already takes AI enablement work seriously. Since March 2026, we have had someone dedicated to this role. We have developed several dozen automations and put considerable effort into integrating Claude Tag (Claude’s Slack add-on) with all the services our staff use.
The work has two halves:
- Automate. Identify recurring work a system can do most of, and build that system. Some automations replace monotonous work we've been doing manually. Others do valuable work we haven't had the internal capacity to do.
- Upskill. Help people use AI themselves: onboarding, written guidance, one-to-one coaching, and shared infrastructure such as skills, prompt libraries, and tool integrations.
A key part of the job is judgment, not building. We could automate far more than one person can build. So you would rank opportunities by their expected value to the organization.
Practicalities
Location: Global, Remote.
Work Hours: Flexible, work between UTC+2 and UTC-8.
Travel Requirement: Employees are expected to attend our 3 in-person retreats each year (locations vary).
Compensation: Annual salary of $125k - $155k USD, exact offer will factor in experience and location. Salaries are not restricted to USD; contracts and payments are usually in local currencies, converted at one-year average exchange rates.
Benefits:
- Comprehensive health insurance program, including supplemental benefits specific to a local country, as available and mandated by local law
- Life insurance and retirement contributions plan, if applicable in your country
- Unlimited PTO with a recommend minimum of 30 days off per year (including public local holidays and vacation time)
- Unlimited personal and sick leave
- Parental leave — up to 4 months of paid leave for permanent staff with at least 12 months of tenure, prorated for those with less tenure, to be taken within the first year after a child’s birth or adoption
- A flexible and generous expense policy for you to spend on equipment and a large range of productivity tools or learning/development opportunities you might find valuable, subject to regulations and manager approval.
- Other benefits as allowed at the discretion of Epoch AI’s leadership and local availability
Application Deadline: Applications are reviewed on a rolling basis. We encourage you to apply early
Start Date: As soon as the successful candidate is available
Reports to: COO
What You'll Own
- Own Epoch’s portfolio of AI automations: keep the production systems running, fix them when they break, and retire the ones that have stopped earning their maintenance.
- Build automations for operations, communications, and research support. Understand colleagues’ needs and involve them in testing and refining the result.
- Prioritize deliberately. Estimate each project’s value, then use that to decide what to build next.
- Run our shared AI tools: Claude in Slack, the integrations that connect it to our systems, and the skills and prompts staff share. Review new tools and models as they come out, and recommend the ones worth adopting. Oversee our AI tool spend.
- Help staff use AI better. Run onboarding for new hires, write practical guidance, and offer one-to-one help. Keep our AI support channels a place where people are comfortable asking basic questions.
- Make each automation straightforward to hand over. Record how it works and how to maintain it, and ensure Epoch controls the accounts and access it depends on.
- Maintain the automations dashboard and report regularly on what changed: which workflows improved, for whom, and by how much.
About You
- Extensive practical experience with AI. You have used coding agents to turn ideas into working tools and can take a project from initial problem to deployment. You experiment with new capabilities and judge whether they improve your work.
- A systematic approach to building with AI agents. You can translate a need into clear specifications, break work into manageable tasks, and assess design trade-offs. You understand how the resulting system fits together and can direct agents to implement it. You don’t need to write the code yourself.
- Product judgment. You start from the needs of the people who will use a tool. You think through how they will interact with it, including less common cases, and make deliberate choices about what to include and leave out. You aim for tools that are coherent, easy to use, and well suited to their purpose.
- Resourcefulness and adaptability. You enjoy exploring unfamiliar problems and finding ways to solve them. You can learn new tools, question an initial approach, and find alternatives when it falls short.
- An aptitude for teaching and coaching. You enjoy helping others become more capable. You listen carefully to understand their work and what they find difficult, adapt your explanations to their experience, and make people comfortable asking questions. You help colleagues apply AI to their own tasks and develop the confidence and judgment to use it independently.
- Autonomy and reliability. You can set your own priorities and follow through without being prompted. You would often be the only person working on a given problem.
- Mission alignment. You understand and share Epoch’s mission.
A background in software engineering is not required. These qualities describe our ideal candidate, but you don’t need to tick every box to be a great fit. If this role interests you and you think you could do it well, we encourage you to apply.
Additional Information
Please submit all of your application materials in English and note that we require professional level English proficiency.
Please do not include a cover letter, photograph, or headshot of yourself, or any personal information that is not relevant to the role for which you're applying (including marital status, age, identity traits, etc.).
Epoch is committed to building an inclusive, equitable, and supportive community for you to thrive and do your best work. We're committed to finding the best people for our team, so please don't hesitate to apply for a role regardless of your age, gender identity/expression, political identity, personal preferences, physical abilities, veteran status, neurodiversity or any other background. Please email [email protected] if you have any questions about this role, accessibility requests, or if you want to request an extension to the application deadline. However, we will not review applications submitted to this email address; please submit your application through the link on this page.
About Epoch AI
Epoch AI is a research institute that investigates trends in machine learning and the economic consequences of AI. Our mission is to develop a comprehensive, publicly accessible knowledge base on AI that informs policymakers, industry leaders, and society at large.
We strive to achieve both rigor and accessibility to our work, as exemplified by some of our most successful projects, including our database of AI models and our AI trends dashboard. Our body of research includes our work on compute trends (IJCN 2022), data scarcity (ICML 2024), and algorithmic progress (NeurIPS 2024). You can read more about our work and mission on our website and in this Time profile.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $125K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Epoch AI, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $125K to $155K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Epoch AI AI Hiring
Epoch AI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $155K - $155K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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