Staff Research Engineer (Multi-Agent Scaling)

• Large teams of agents are starting to solve problems no single agent can, from rewriting major codebases to formalizing landmark mathematics
• Our team studies how these teams scale: what happens to performance, cost and coordination as the number of agents, the compute budget and the length of the task grow, and what has to change to keep getting returns from that scale
• We build the platform and evaluations Anthropic uses to run and measure large agent teams, and other research teams build on them
• This role lives at the boundary between research and engineering
• It is a generalist role on a small team: you’ll design and run large experiments, build the systems they run on, and get to the bottom of surprising results
• We often need to go from a vague question to a running experiment quickly
• 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
• Representative projects:
• Measure how performance scales with the number of agents on a hard problem, and explain where the curve bends and why
• Prepare our largest-ever agent run: find what breaks as team size and task length grow together, and fix it before launch
• Work out how to allocate a fixed compute budget across a team of agents to solve a problem fastest
• Build tooling that turns the activity of a large agent team into something a researcher can read in minutes
• Design a novel eval that distinguishes real gains in teamwork from artifacts of the evaluation setup

Benefits

• Comprehensive health, dental, and vision insurance for you and your dependents
• Inclusive fertility benefits via Carrot Fertility
• 22 weeks of paid parental leave
• Flexible paid time off and absence policies
• Mental health support for you and your dependents
• Competitive salary and equity packages
• Optional equity donation matching at a 1:1 ratio, up to 25% of your equity grant
• Retirement plans with competitive matching
• Life and income protection plans
• $500/month flexible wellness and time saver stipend
• Commuter benefits
• Annual education stipend
• Home office stipends
• Relocation support for those moving for Anthropic
• Daily meals and snacks in the office- Care about the societal impacts of your work
• Have owned something substantial end to end, such as a large system, an evaluation or benchmark, an agent product, or a research project
• Are results-oriented, with a bias towards flexibility and impact
• Have clear written and verbal communication
• Can work from a vague question rather than a spec
• Have significant software engineering, ML or research engineering experience
• Genuinely enjoy both research and engineering work
• Think quantitatively about complex systems, and think twice before trusting a number
• We encourage you to apply even if you do not believe you meet every single qualification
• We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team
• 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
• Experience building or operating large-scale distributed systems, such as schedulers, sandboxed code execution, or inference and RL infrastructure
• Experience with scaling laws or other large-scale empirical research
• Built evaluations, benchmarks or harnesses for LLMs or agents
• A background in operations research, statistics, economics, physics, quantitative finance, or another field that models and optimizes complex systems
• Experience building complex agentic systems that use LLMs