Staff Research Engineer, Multi-Agent Scaling

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. How we're 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. We're 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.