You may be a good fit if you:

• Have significant software engineering experience
• 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
• Like working on systems and tools that make other people more productive
• 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
• Implementing LLM finetuning algorithms, such as RLHF

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 finetuning systems 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
• Implementing a stable, fast version of a new training algorithm proposed by a researcher

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