Software Engineer, Research Productivity

About the role

Anthropic's Research Productivity organization builds the infrastructure and systems that accelerate research across Anthropic, including the tools that help our research process get faster and more effective over time. We're looking for an experienced software engineer to join us as we scale this infrastructure at a moment when both demand and scope are growing extremely quickly.

This team operates like a startup within a startup. You'll be a strong fit if you like thinking from first principles, iterating fast on infrastructure, and tackling reliability and scalability challenges head-on as the products built on top of your systems evolve underneath you.

You'll independently scope complex, multi-month projects, drive cross-org alignment through ambiguous problem spaces, and make the architectural decisions that shape how the infrastructure behind our research tooling gets built. You'll partner directly with research teams to understand their workflows, anticipate how their requirements will change, and design and scale infrastructure that can keep pace.

Responsibilities

• Design, build, and scale infrastructure and systems that support rapidly increasing usage, where requirements and workload continue to evolve as the products built on top of them evolve
• Independently scope and lead complex, multi-month engineering projects, from an ambiguous starting point through to a production system
• Drive cross-organizational alignment on technical direction, working through ambiguous problem spaces with multiple stakeholders and teams
• Make architectural decisions that shape the foundation of research infrastructure and tooling across Anthropic
• Partner directly with researchers to deeply understand their workflows, then anticipate and design for how those needs will change
• Iterate quickly, favoring pragmatic, first-principles solutions and fast feedback loops over heavy upfront design
• Take ownership of the reliability and scalability of critical systems as load, usage, and complexity increase
• Help set technical standards and best practices for the team, and mentor other engineers

Minimum Qualifications

• Experience designing, building, and operating large-scale distributed systems or infrastructure in production
• A track record of independently scoping and delivering complex, ambiguous, multi-month technical projects
• Strong software engineering fundamentals and hands-on coding ability
• Experience making architectural decisions that other engineers and teams build on top of
• Strong written and verbal communication skills, with experience driving alignment across multiple teams or stakeholders
• Demonstrated ability to operate effectively in ambiguous, fast-changing environments

Strong candidates may also have

• Experience building infrastructure or platforms specifically for research or machine learning workflows
• Direct experience navigating the reliability and architectural challenges that come with rapidly scaling systems
• Experience with distributed systems, cloud infrastructure, and infrastructure-as-code
• Familiarity with the compute, tooling, and workflow needs of large-scale machine learning research
• Experience operating in a startup or startup-like environment, i.e. a small, fast-moving team with high autonomy
• Prior experience as a technical lead or mentor for other engineers