Post-Training Research Engineer

Highlights

• Build in-house tooling powering post-trained custom models at inference scale.
• Optimize efficiency and quality across diverse model architectures and techniques.
• Support mission-critical inference for leading AI companies like Cursor, Notion.

What they offer

• 100% coverage of medical, dental, and vision insurance for employee and dependents
• Flexible PTO policy including company-wide Winter Break
• Paid parental leave
• Fertility and family-building stipend through Carrot (U.S. only)
• Company-facilitated 401(k)

Ideal candidate

ML-minded engineer who loves diving into messy, cross-stack problems and partnering with researchers to ship efficient, high-quality custom models into production at scale.

Requirements

• 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, containerization, and networking protocols
• A sense of creativity and willingness to ask difficult questions about our approach, assumptions, and tooling choices