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AnthropicAnthropicSan Francisco, CA

Software Engineer, Research Infrastructure

Build and scale research infrastructure and distributed systems at Anthropic to accelerate ML research workflows. Independently lead complex multi-month projects, drive cross-org alignment, and partner with researchers in a fast-evolving, ambiguous environment.

405k – 625k
Hybrid7+ YOEML Engineering

About the role

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

Preferred Qualifications

  • 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

Compensation

Annual Salary: $405,000—$625,000 USD

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Skills

Distributed SystemsLarge-Scale InfrastructureCloud InfrastructureInfrastructure As CodeMachine Learning WorkflowsResearch Infrastructure

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