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 [redacted].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.