Staff Research Engineer, Multi-Agent Scaling (AI)

Location: San Francisco, CA; New York City, NY; or Seattle, WA. Hybrid attendance is required, with staff expected to be in an office at least 25% of the time.

Annual salary: $500,000–$850,000 USD

Company

Anthropic develops reliable, interpretable, and steerable AI systems intended to be safe and beneficial for users and society.

What you will do

• Design, run, and interpret large-scale experiments on teams of AI agents.
• Investigate how performance and efficiency change with team size, compute budgets, and task horizons.
• Build and scale reliable systems for running very large agent teams and debug failures that emerge at scale.
• Design trustworthy evaluations for long-horizon problems and build tooling and metrics that make agent activity understandable.
• Partner with research teams across Anthropic so they can run experiments on the platform and communicate findings clearly.

Requirements

• Significant software engineering, machine learning, or research engineering experience.
• End-to-end ownership of a substantial system, evaluation or benchmark, agent product, or research project.
• Ability to combine research and engineering, reason quantitatively about complex systems, and work from vague questions.
• Clear written and verbal communication, with a results-oriented and flexible approach.
• Bachelor’s degree or equivalent education, training, or experience in a relevant field; formal certifications and academic publications are not required.

Nice to have

• Experience with large-scale distributed systems, schedulers, sandboxed code execution, inference infrastructure, or reinforcement learning infrastructure.
• Experience building LLM or agent evaluations, benchmarks, harnesses, or complex agentic systems.
• Experience with scaling laws or large-scale empirical research.
• Background in operations research, statistics, economics, physics, quantitative finance, or another field focused on modeling and optimizing complex systems.
• Prior multi-agent systems or reinforcement learning experience is not required.

Culture & Benefits

• Collaborative research environment focused on large-scale empirical AI research and steerable, trustworthy systems.
• Frequent research discussions and cross-team collaboration.
• Competitive compensation, optional equity donation matching, generous vacation and parental leave, and flexible working hours.
• Visa sponsorship is available, with immigration-lawyer support, although sponsorship cannot be guaranteed for every role or candidate.