# Research Engineer, Pretraining Scaling at Anthropic

- Company: Anthropic (anthropic.com)
- Location: San Francisco, CA
- Places: San Francisco, United States
- Posted: 2025-09-30
- Apply: https://job-boards.greenhouse.io/anthropic/jobs/4938432008
- Page: https://ye.saranatour.dev/jobs/research-engineer-pretraining-scaling-anthropic-kn755hmy53qb83np6ge7max4f58hda1y

## The posting

Research Engineer, Pretraining Scaling
Anthropic · San Francisco, CA

## **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 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
- 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
- Experien