ML Safeguards Infra Engineer — Scale Research Tools

Anthropic Limited; San Francisco (CA); Ibrido; USD 350.000 - 500.000; Tempo pieno

Descrizione del lavoro

Anthropic Limited is seeking an engineer to own the infrastructure behind Safeguards research, building tooling, pipelines, and interfaces that enable fast iteration and reliable results as models evolve.

You'll work between research and production, scale data workflows, and collaborate with researchers and engineers to improve throughput, cost efficiency, and reliability of large-scale inference and scoring workloads.

Competenze

• Strong software engineering fundamentals and coding ability.
• Experience building data-intensive or distributed systems in production.
• Experience building tooling or infrastructure used by other engineers or researchers.
• Comfort moving from research experiments to production systems.
• Ability to debug performance and correctness across stacks.
• Strong written and verbal communication; collaborative decisions.

Mansioni

• Build and scale the infrastructure and data pipelines behind Safeguards machine learning research.
• Own the training, evaluation, and scoring workflows for researchers, focusing on faster iteration.
• Design tooling and interfaces, including libraries and CLI tools, for researchers to use without deep systems knowledge.
• Build correctness and observability into the stack to keep results trustworthy as workloads evolve.
• Take high-value research workflows from experiments to production-grade jobs.
• Improve throughput, cost, and reliability of large-scale inference and scoring workloads.

Conoscenze

Python

Distributed systems

Tooling & infrastructure

Research-to-prod

Team collaboration

Formazione

Bachelor’s degree

Descrizione del lavoro

Anthropic Limited is seeking an engineer to own the infrastructure behind Safeguards research, building tooling, pipelines, and interfaces that enable fast iteration and reliable results as models evolve.

You'll work between research and production, scale data workflows, and collaborate with researchers and engineers to improve throughput, cost efficiency, and reliability of large-scale inference and scoring workloads.