Research Engineer, Pretraining Scaling
Research Engineer, Pretraining Scaling
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$ 315000 - 560000
1 Vacancy
Job Description
About Anthropic
Anthropics 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:
Anthropics ML Performance and Scaling team trains our production pretrained models work that directly shapes the companys future and our mission to build safe beneficial AI systems. As a Research Engineer on this team youll 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. Youll 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 cant 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 stackfrom 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
- Collaborate closely with teammates across SF and London as well as with Tokens Architectures and Systems teams
- Contribute to the teams 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 workyoud 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 LLMs or working extensively with JAX/TPU PyTorch or other ML frameworks at scale
- Contributed to open-source LLM frameworks (e.g. openlm 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 operationalyoull 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. Youll gain hands-on experience with some of the largest most sophisticated training runs in the industry. Youll work alongside world-class researchers and engineers and the institutional knowledge you build will compound in ways that cant be easily transferred. For people who thrive on this type of work its uniquely rewarding.
Were building a close-knit team of people who genuinely care about doing excellent work together. If youre someone who wants to be part of training the models that will define the future of AIand youre excited about the full reality of what that entailswed 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 expectedbase compensation for this position is below. Our total compensation package for full-time employees includes equity benefits and may include incentive compensation.
Annual Salary:
$315000 - $560000 USD
Logistics
Education requirements: We require at least a Bachelors degree in a related field or equivalent experience.
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 arent 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 youre interested in this work. We think AI systems like the ones were 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.
How were 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. Were 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 aboutour policyfor using AI in our application process
Employment Type
Full Time
