Job description

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's research organization works across the full model development lifecycle, from pre-training and post-training to alignment, interpretability, and safety, each operating at the frontier of AI development. As a Technical Program Manager for Research, you'll define and build the programs that research teams need most. You'll move across research areas like compute, evals, RL environments, and emerging research initiatives, going deep enough in each to understand how researchers work and what they need. You'll identify where the biggest opportunities for impact lie, find the highest-leverage gaps, and build the programs, processes, and tooling that allow researchers to focus on research. This is a 0-to-1 role: you'll explore new domains as priorities shift, determine what each one needs, and create lasting impact where none existed before. Note: This role may require responding to incidents on short-notice, including on weekends. Responsibilities Embed deeply within a research domain to understand the technical landscape, build trust with researchers and technical leaders, and identify the highest-leverage problems to solve, knowing the surface area will shift over time as research priorities evolve Move fluidly across research areas like compute, evals, RL environments, and emerging research initiatives, picking up new domains quickly and getting to depth fast Drive end-to-end execution of complex, ambiguous research initiatives spanning multiple teams, often without established playbooks or precedent Establish processes and frameworks that bring structure to unstructured research environments without slowing researchers down Lead efforts like large-scale compute resource planning, including allocation, efficiency, and prioritization across research and production workstreams Drive eval readiness for model launches by standardizing results, shaping eval plans early, improving tooling, and ensuring honest, transparent reporting across research, product, and marketing Own execution and operational health of RL environments across major training runs, coordinating cross-team trade-offs and feeding insights back into roadmap planning Equip research leadership to make decisions quickly by going deep on technical tradeoffs and presenting clear, actionable recommendations Act as the connective tissue between research, engineering, and product teams to reduce chaos and accelerate execution You May Be a Good Fit If You Have a background in ML research or engineering with several years of experience building technical programs from scratch, ideally with hands-on exposure to training, evaluation, or large-scale distributed systems Are a fast learner who can ramp on unfamiliar technical domains quickly and contribute meaningfully to discussions with researchers Are resourceful, high-agency, and able to navigate ambiguity and shifting priorities to drive progress in fast-moving research environments Have a track record of operational ownership of complex technical systems, including monitoring, incident response, and performance optimization Can reason about technical tradeoffs at depth across model architecture, training infrastructure, evals, or compute efficiency, and translate them into clear decisions for leadership Have excellent stakeholder management skill and the ability to influence senior technical staff through competence and consistent delivery Are comfortable with high-stakes environments where decisions impact compute spend, model training timelines, and launch outcomes Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems Are excited to redefine what technical program management looks like at the frontier of AI research 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: $365,000 — $435,000 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.

Requirements

Must-haves

Move fluidly across research areas like compute, evals, RL environments, and emerging research initiatives, picking up new domains quickly and getting to depth fast
Drive end-to-end execution of complex, ambiguous research initiatives spanning multiple teams, often without established playbooks or precedent
Establish processes and frameworks that bring structure to unstructured research environments without slowing researchers down
Lead efforts like large-scale compute resource planning, including allocation, efficiency, and prioritization across research and production workstreams
Drive eval readiness for model launches by standardizing results, shaping eval plans early, improving tooling, and ensuring honest, transparent reporting across research, product, and marketing
Own execution and operational health of RL environments across major training runs, coordinating cross-team trade-offs and feeding insights back into roadmap planning
Equip research leadership to make decisions quickly by going deep on technical tradeoffs and presenting clear, actionable recommendations
Act as the connective tissue between research, engineering, and product teams to reduce chaos and accelerate execution
You May Be a Good Fit If You
Have a background in ML research or engineering with several years of experience building technical programs from scratch, ideally with hands-on exposure to training, evaluation, or large-scale distributed systems
Are a fast learner who can ramp on unfamiliar technical domains quickly and contribute meaningfully to discussions with researchers
Are resourceful, high-agency, and able to navigate ambiguity and shifting priorities to drive progress in fast-moving research environments
Have a track record of operational ownership of complex technical systems, including monitoring, incident response, and performance optimization
Can reason about technical tradeoffs at depth across model architecture, training infrastructure, evals, or compute efficiency, and translate them into clear decisions for leadership
Have excellent stakeholder management skill and the ability to influence senior technical staff through competence and consistent delivery
Are comfortable with high-stakes environments where decisions impact compute spend, model training timelines, and launch outcomes
Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
Are excited to redefine what technical program management looks like at the frontier of AI research
The annual compensation range for this role is listed below.
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.
How we're different

Nice-to-haves

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Keywords to mirror

aiml

Platform: greenhouse
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Benefits

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