Anthropic
Machine Learning Infrastructure Engineer, Safeguards Research
About this role
Anthropic seeks a Machine Learning Infrastructure Engineer to build and maintain the systems powering their Safeguards research team, enabling researchers to rapidly experiment with detection methods that identify and mitigate AI model misuse. You'll own data pipelines, training workflows, and tooling that bridge research exploration and production deployment, while solving novel systems challenges at scale.
What you'll do
- Build and scale infrastructure and data pipelines supporting Safeguards ML research
- Own training, evaluation, and scoring workflows to minimize time from idea to results
- Design researcher-facing libraries and CLI tools that abstract underlying system complexity
- Implement correctness and sanity checks to maintain result trustworthiness as models evolve
- Transition high-value research workflows from experiments to production-grade systems
- Optimize throughput, cost, and reliability of large-scale inference and scoring workloads
What they're looking for
- Python and strong software engineering fundamentals
- Building and operating data-intensive or distributed systems in production
- Debugging performance and correctness issues across unfamiliar systems
- ML infrastructure and experiment tracking systems
- GPU/accelerator programming and inference optimization
- Language models, transformers, and model internals
- Collaborative technical communication and cross-functional partnership
- Research-to-production pipeline experience
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Anthropic
Anthropic builds Claude, an AI assistant, and is hiring for engineering roles across infrastructure, data systems, and security that support both AI research operations and the company's internal technology needs. The company seeks infrastructure engineers, systems integrators, data scientists, and security specialists to build production-scale systems for training data pipelines, financial operations, developer productivity measurement, research infrastructure, and server firmware security.
- Website
- anthropic.com
Likely interview questions
- Describe a large-scale data or distributed system you built in production—what were the key challenges and how did you solve them?
- Tell us about a time you designed infrastructure or tooling for researchers or engineers to use. How did you balance their needs with technical constraints?