Interested in this AI/ML Engineer role at Anthropic?
Apply Now →Skills & Technologies
About This Role
About Anthropic
-------------------
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the role
Anthropic is seeking an AI Fluency Education Lead to create courses and content that teach people how AI works and how to work well with AI. Most people right now pick up AI wherever they happen to find it: a prompt from a coworker, a video promising ten hacks, or a screenshot from LinkedIn. It’s hard to discern which tidbits are short term tactics and what skills will be enduring. AI Fluency is our work to close that gap: teaching how AI actually works, and how to work well alongside it, so people can make their own decisions and judgements with AI instead of copying someone else’s. Note that this does not cover product training. We recognize that most people do not use a single tool or single model, so AI Fluency is deliberately model agnostic. We teach durable mindsets and understandings that help someone use AI well and safely, no matter what system is in front of them.
Much of this role is about creation: writing, filming, prototyping, and publishing. It’s also about building the systems that let that work multiply and reach the right audiences. We can't teach every audience ourselves, so you'd design a modular library — short videos, exercises, frameworks, one\-pagers, all free and openly licensed — along with the AI\-assisted pipelines that remix and assemble those pieces into something specific for a given learner. You will also work with other teams to decide which audiences we go after, which topics we prioritize, and which formats we pursue. You may also invent formats that don't exist yet, because most AI education still looks like an online course from 2015 and we don't think the subject and the medium should be that far apart.
You would work closely with a sister group on the education team that researches what fluency means and how to measure it. You’re the person who takes that and turns it into something someone can actually learn from.
Key responsibilities
- Own the curriculum for general\-public AI fluency end to end — the roadmap, the sequencing, the quality bar, and the call on what we teach and what we deliberately leave out.
- Curate and create the AI fluency material itself — curricula, courses, videos, essays, exercises, interactive lessons — and set the quality bar for any partner content creation engagements
- Build AI\-assisted production pipelines so the distance from an idea to a published piece keeps getting shorter while the quality keeps going up
- Turn research frameworks into material that resonates for people whose jobs look nothing like ours: nurses, teachers, small business owners, policymakers, community leaders, and so on
- Learn from and co\-create with outside institutions, such as educators, public\-sector organizations, and community groups
- Prototype learning formats that only work with AI in the loop — personalized paths, conversational practice, assessment that adapts to the learner — and get rough versions in front of real people in days rather than quarters
- Measure whether the teaching worked — completion is not comprehension, and comprehension is not changed behavior. Define what learning actually looks like for this audience and instrument for it.
You may be a good fit if you have
- Deep experience designing content and curricula for adult learners at significant scale, with real taste for what makes learning stick when you’re not in the room.
- Exceptional writing for general audiences, and specifically the knack for turning a technical idea into a mental model that's accurate and also intuitive to novices and those who have never seen it before
- Experience making multimedia learning content end to end — scripts, video, interactive — and working with agencies and partners to deliver on multiple projects in short deadlines
- A working practice of using Claude and other LLMs as infrastructure in your own production
- Enough technical comfort to build light tooling and automations yourself; this isn't an engineering role, but you shouldn't be intimidated by adding to the pipeline that produces your work
- Experience\-derived opinions about pedagogy you'll argue for, and the willingness to drop them when the data says otherwise
- Genuine satisfaction in making things other people teach, such as a teacher taking your material, changing it for their audience, and delivering education without your oversight
- Comfort in a fast\-moving environment where you're building the process as you go
You don't need a background in AI or a degree in education, but you do need to have made people fluent in something before at scale.
Strong candidates may also have
- Built an education program or content function from nothing
- Experience in public education, civic technology, or policy communication.
- A background in AI/ML education, learning science, cognitive science, or behavioral research
- Experience co\-creating content with outside institutions and partners, not only with vendors and production agencies
- Experience building AI\-augmented content pipelines
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary:
$270,000 \- $365,000 USD
Logistics
-------------
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
Location\-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
How we're different
-----------------------
We believe that the highest\-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large\-scale research efforts. And we value impact — advancing our long\-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest\-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT\-3, Circuit\-Based Interpretability, Multimodal Neurons, Scaling Laws, AI \& Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
----------------------
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
Salary Context
This $270K-$365K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Anthropic, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($317K) sits 48% above the category median. Disclosed range: $270K to $365K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Anthropic AI Hiring
Anthropic has 6 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Architect, Data Scientist. Positions span San Francisco, CA, US, Remote, US. Compensation range: $300K - $485K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.