Strategic Overview
Technical Program Manager, RL Research at Anthropic is a product opportunity centered on the specialist responsibilities and outcomes described in the employer's listing.
The published role emphasizes work such as Deliver a regular read on the ground truth in RL research, covering performance against baselines, experiment results, day-to-day health, and incidents Work with RL org leads on prioritizing, ranking, and tracking the state of experiments.
The role is listed for San Francisco, CA / New York City, NY with the working arrangement published with the role, giving candidates the practical context needed to assess fit before applying.
Key information at a glance
About This Role
Our Reinforcement Learning teams are central to advancing our AI systems, contributing to every Claude model and driving the autonomy and coding gains in our latest releases. The work spans computer use, code generation through RL, fundamental RL research for large language models, scalable infrastructure and training methodologies, and model reasoning.
Research TPM team supports the full model development lifecycle, from pre-training through post-training, operating at the frontier of AI development.
Key Responsibilities & Objectives
- Deliver a regular read on the ground truth in RL research, covering performance against baselines, experiment results, day-to-day health, and incidents
- Work with RL org leads on prioritizing, ranking, and tracking the state of experiments
- Drive research reviews end to end in partnership with set the agenda, make sure the right context is in the room ahead of time, and close the loop on what gets decided
- Establish processes and frameworks that bring structure to an unstructured research setting without slowing researchers down
Required Qualifications & Candidate Blueprint
- Have a background in ML engineering or ML research before transitioning to technical program management
- Have deep, hands-on experience with ML training pipelines, RLHF systems, and large-scale data infrastructure in production
- Have a track record of building execution plans and inventing high-leverage processes that reduce operational overhead and let researchers focus on research
Salary & Benefits
Published compensation: $365,000 – $435,000 USD
- Deliver a regular read on the ground truth in RL research, covering performance against baselines, experiment results, day-to-day health, and incidents
Salary Benchmark
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Full Job Description
The following is the complete job description supplied by the source. We sanitize unsafe markup and remove external links, but do not shorten or rewrite the employer's source content.
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 Team
Our Reinforcement Learning teams are central to advancing our AI systems, contributing to every Claude model and driving the autonomy and coding gains in our latest releases. The work spans computer use, code generation through RL, fundamental RL research for large language models, scalable infrastructure and training methodologies, and model reasoning.
Research TPM team supports the full model development lifecycle, from pre-training through post-training, operating at the frontier of AI development.
About the Role
As a Technical Program Manager on the reinforcement learning team, you will own the systems and programs that determine how fast our research moves: a trustworthy read on the state of RL research, the review and prioritization processes that turn that read into critical decision for production RL runs. Strong candidates should have an ML engineering or research background and have grown into program leadership. You'll need real technical depth: the ability to debug data pipelines, read RL transcripts to spot issues, and make allocation and quality decisions in real time when research or production runs hit problems. You'll need organizational effectiveness in equal measure: the ability to navigate a fast-growing organization, quickly identify the critical people and teams across research, infrastructure, product, and data operations, and coordinate across them without losing velocity.
Join us in our mission to build AI systems that are safe, reliable, and beneficial to humanity.
Responsibilities
- Deliver a regular read on the ground truth in RL research, covering performance against baselines, experiment results, day-to-day health, and incidents
- Work with RL org leads on prioritizing, ranking, and tracking the state of experiments
- Drive research reviews end to end in partnership with set the agenda, make sure the right context is in the room ahead of time, and close the loop on what gets decided
- Establish processes and frameworks that bring structure to an unstructured research setting without slowing researchers down
- Collaborate with research leads, infrastructure engineers, and data operations to identify blockers, prioritize competing needs, and make technical trade-off decisions
You May Be a Good Fit If You
- Have a background in ML engineering or ML research before transitioning to technical program management
- Have deep, hands-on experience with ML training pipelines, RLHF systems, and large-scale data infrastructure in production
- Have a track record of building execution plans and inventing high-leverage processes that reduce operational overhead and let researchers focus on research
- Are a fast learner who builds deep contextual understanding in unfamiliar technical domains and can contribute meaningfully to discussions with researchers
- Are resourceful, high-agency, and able to navigate ambiguity and shifting priorities to drive progress in a fast-moving research setting
- Have excellent stakeholder management and communication skills, with the ability to influence senior technical staff through clarity, competence, and consistent delivery
- Are excited about pushing the frontier of what RL can do at scale
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.
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.
Editor’s Market Perspective (Our Custom Analysis)
Why This Position Matters
This product opening is a useful opportunity for candidates whose experience matches the role-specific capabilities named in the listing. Its strongest career signal is the scope of the work itself — particularly Deliver a regular read on the ground truth in RL research, covering performance against baselines, experiment results, day-to-day health, and incidents — rather than a generic title alone.
Resume & Application Strategy
For the application, mirror the employer's language where it truthfully matches your background: foreground the role-specific capabilities named in the listing, quantify outcomes from comparable work, and place the most relevant experience near the top of the resume. If the listing specifies tools, platforms, leadership scope, or domain experience, use those exact terms in context instead of presenting an unrelated keyword list.
We organize source-provided information into this independent job-seeker overview. Compensation is shown only when the source provides it; the official listing remains the authority for current requirements and application instructions.