Machine Learning Infrastructure Engineer, Core Resources

About the role:

You want to scale cutting-edge systems to build AI models like Claude. You're excited to work at the frontier of machine learning and infrastructure, implementing and improving our systems to create ever more capable, reliable and steerable AI. As an ML Infra Engineer on our Core Resources team, you'll be responsible for optimizing infrastructure that our researchers depend on to train models and your work will directly enable breakthroughs in AI capabilities and safety. You'll focus obsessively on improving the performance, robustness, usability, and efficiency of these systems so our research can progress as quickly as possible while maximizing ROI on our resources. You're energized by the challenge of supporting and empowering our research teams in the mission to build beneficial AI systems.

You may be a good fit if you:

• Have 8+ years of software engineering experience
• Like working on systems and tools that make other people more productive
• Like working cross-functionally with finance and other business-facing teams
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High performance, large scale distributed systems
• Kubernetes
• Python
• Machine learning
• LLM inference

Representative projects:

• Profiling our reinforcement learning pipeline to find opportunities for improvement
• Building a system that regularly launches training jobs in a test environment so that we can quickly detect problems in the training pipeline
• Making changes to our ML infrastructure so they work on new model architectures
• Building instrumentation to detect and eliminate Python GIL contention in our training code
• Diagnosing why training runs have started slowing down after some number of steps, and fixing it

Deadline to apply: None. Applications will be reviewed on a rolling basis.