Post-Training Research Engineer

About Baseten

Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We recently raised our $1.5B Series F led by Altimeter Capital, Conviction Partners, and Spark Capital.

We are looking for an engineer with strong experience in machine learning and solid foundations in math and computer science to join our growing Post-Training team at Baseten.

The Role

Custom models are instrumental to the success of Baseten customers. The Post-Training team is responsible for the success of our customers’ post-trained models, employing techniques like reinforcement learning, supervised finetuning, and in-house research techniques to produce models that are more efficient and higher quality than closed source models.

Your role as a research engineer is to build the in-house tooling to support all of this. We care about training a wide spectrum of different model architectures with a variety of techniques efficiently and at scale. At times this involves zooming deep into a particular technical topic, but more often it involves working across the stack as a whole - systems-level concepts like Kubernetes, cgroups, storage systems, and networking topologies, as well as PyTorch distributed tensor computation, and GPU kernels.

Qualifications

• 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, and context parallelism
• Experience and knowledge to profile and improve the performance of a distributed GPU program in PyTorch or a similar library
• 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, and GPUDirect
• Solid fundamentals in operating systems concepts like processes, files, kernel drivers, containerization, and networking protocols
• A sense of creativity and willingness to ask difficult questions about our approach, assumptions, and tooling choices