About the role:

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.
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

You may be a good fit if you:

• 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

Strong candidates may also have:

• 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

Strong candidates need not have:

• Formal certifications or education credentials
• Academic research experience or publication history
• Prior experience with multi-agent systems or reinforcement learning

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

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:

$500,000-$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.

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