Machine Learning Performance Engineer
Jane Street13 days ago
Responsibilities
- Optimize the performance of machine learning models for training and inference.
- Enhance large-scale training efficiency and low-latency inference in real-time systems.
- Utilize a whole-systems approach to improve storage, networking, and GPU-level performance.
- Debug and optimize training runs' performance end to end.
- Evaluate throughput and goodput at the lowest system levels.
Requirements
- Experience in low-level systems programming and optimization.
- Understanding of modern machine learning techniques and toolsets.
- Low-level GPU knowledge including PTX, SASS, and Tensor Cores.
- Experience with debugging and optimization tools like CUDA GDB and NSight.
- Familiarity with libraries such as Triton, cuDNN, and cuBLAS.
- Background in networking technologies like Infiniband and NVLink.
About Jane Street
Jane Street is a quantitative trading firm with offices in New York, London, Hong Kong, Singapore, and Amsterdam. We are always recruiting top candidates and we invest heavily in teaching and training. The environment at Jane Street is open, informal, intellectual, and fun. People grow into long careers here because there are always new and interesting problems to solve, systems to build, and theories to test. More than twenty years after our founding, it still feels like we’re just getting started. Jane Street does not offer any services to individual investors: https://www.janestreet.com/fraud-and-impersonation-warnings/