Post-Training Research Engineer

Details

• Dense, on-policy or both?
• Repeated kv cache for long-running agents
• Distillation without the dark – replicating black-box on-policy distillation on Baseten
• A deep understanding of modern ML techniques and tools for training transformers
• Advanced experience in a tensor/array computation library like PyTorch, TensorFlow, Jax, or similar
• A detailed understanding of transformer training parallelism strategies like data parallelism, sharded data parallelism, tensor parallelism, pipeline parallelism, context parallelism
• The experience and knowledge to profile and improve the performance of a distributed GPU program in PyTorch or a similar library
• The ability to perform roofline analysis on a transformer training setup
• A willingness to dive into messy problems, work with researchers, derive specifications by asking important questions, and execute
• Familiarity with HPC and distributed computing platforms like Slurm, Ray, Kubernetes, and Dask
• Familiarity with cluster networking technology like Infiniband, RoCE, GPUDirect
• Solid fundamentals in operating systems concepts like processes, files, kernel drivers, containerisation, and networking protocols
• A sense of creativity and willingness to ask difficult questions about our approach, assumptions, and tooling choices
• Competitive compensation, including meaningful equity.
• 100% coverage of medical, dental, and vision insurance for employee and dependents
• Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
• Paid parental leave
• Fertility and family-building stipend through Carrot
• Company-facilitated 401(k)
• Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.