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 ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.
This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow.
Responsibilities:
Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability
Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure
Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance
Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams
Build and maintain production logging, monitoring dashboards, and evaluation infrastructure
Add new capabilities to the training codebase, such as long context support or novel architectures
Don't want to miss the next one?
Subscribe to daily email alerts for roles matching your interests.
Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams
Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned
You May Be a Good Fit If You:
Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems
Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other
Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure
Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs
Excel at debugging complex, ambiguous problems across multiple layers of the stack
Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents
Are passionate about the work itself and want to refine your craft as a research engineer
Care about the societal impacts of AI and responsible scaling
Strong Candidates May Also Have:
Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale
Contributed to open-source LLM frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax)
Published research on model training, scaling laws, or ML systems
Experience with production ML systems, observability tools, or evaluation infrastructure
Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence
What Makes This Role Unique:
This is not a typical research engineering role. The work is highly operational—you'll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.
However, this operational intensity comes with extraordinary learning opportunities. You'll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You'll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can't be easily transferred. For people who thrive on this type of work, it's uniquely rewarding.
We're building a close-knit team of people who genuinely care about doing excellent work together. If you're someone who wants to be part of training the models that will define the future of AI—and you're excited about the full reality of what that entails—we'd love to hear from you.
Location:This role requires working in-office 5 days per week in San Francisco.
Deadline to apply: None. Applications will be reviewed on a rolling basis.
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:
$350,000—$850,000 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 va
Similar roles you might like
More openings like this one — take a look before you go.
