Staff Research Engineer, Multi-Agent Scaling
San Francisco, CA | New York City, NY | Seattle, WA · New York City, NY · San Francisco · Seattle, WA · USD 500,000-850,000 / year · Hybrid · Posted Oct 2, 2026
At a glance
- Workplace
- Hybrid
- Location
- San Francisco, CA | New York City, NY | Seattle, WA · New York City, NY · San Francisco · Seattle, WA
- Salary
- USD 500,000-850,000 / year · Base salary
- Visa sponsorship
- Visa sponsorship available
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
- Large teams of agents are starting to solve problems no single agent can, and this role studies how these teams scale.
- This role lives at the boundary between research and engineering, acting as a generalist on a small team to design and run large experiments.
Responsibilities
- Design, run and interpret large-scale experiments on agent teams, reasoning rigorously about what the data does and doesn't show
- Investigate how performance and efficiency change as team size, compute and task horizon grow, and find the bottlenecks that limit them
- Build and scale the systems that run very large agent teams reliably, and debug the failures that only appear at scale
- Design evaluations for long-horizon problems, and keep their results trustworthy
- Build the tooling and metrics that let researchers see what a large agent team is doing and why
- Partner with research teams across Anthropic so they can run their own experiments on the platform, and communicate findings clearly
Requirements
- Have significant software engineering, ML or research engineering experience
- Have owned something substantial end to end, such as a large system, an evaluation or benchmark, an agent product, or a research project
- Genuinely enjoy both research and engineering work
- Think quantitatively about complex systems, and think twice before trusting a number
- Can work from a vague question rather than a spec
- Are results-oriented, with a bias towards flexibility and impact
- Have clear written and verbal communication
- Care about the societal impacts of your work
Preferred qualifications
- Experience building or operating large-scale distributed systems, such as schedulers, sandboxed code execution, or inference and RL infrastructure
- Built evaluations, benchmarks or harnesses for LLMs or agents
- Experience building complex agentic systems that use LLMs
- Experience with scaling laws or other large-scale empirical research
- A background in operations research, statistics, economics, physics, quantitative finance, or another field that models and optimizes complex systems
Benefits
- optional equity donation matching
- generous vacation
- parental leave
- flexible working hours
Workplace
- Location-based hybrid policy: expect all staff to be in one of our offices at least 25% of the time
Skills
- LLMs
- Distributed Systems
- Reinforcement Learning
