Machine Learning Compiler Engineer at Jane Street (New York, NY)
Jane Street· New York, NY·
Job description
About the position
We’re looking for compiler engineers to join a new ML Compilers Team at Jane Street.
Machine learning is a critical pillar of our global business. Our ever-evolving trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing us to incorporate new ideas with relatively little friction. Our ML team is full of people with a shared love for the craft of software engineering, and for designing APIs and systems that are delightful to use.
ML compilers let us quickly turn research ideas and novel architectures into high-throughput training jobs and low-latency inference. To get the most out of our hardware resources and enable rapid model development and deployment, we are working to understand and improve the current ML compiler technologies and explore where it makes sense to build our own solutions. In this role, you would shape the development of our compiler stack.
If you’ve never thought about a career in finance, you’re in good company. Many of us were in the same position before working here. If you have a curious mind and a passion for solving interesting problems, we have a feeling you’ll fit right in.
About you
The ideal candidate has worked on an ML compiler in the past, but non-ML compiler experience is also valuable. Experience with any part of the stack is fine, be it high-level ML frameworks (PyTorch, Jax, TensorFlow), graph compilers (Inductor, XLA, TVM), or kernel DSLs (Triton, Helion, cuTile). You should understand how all those pieces fit together even if you are unfamiliar with specific parts.
Some other things you might be excited about are:
- Optimizations in ML compilers: algebraic rewriting, materialize vs recompute tradeoffs and fusion, equality saturation, scheduling languages, loop-level optimization, autotuning
- ML compiler frontends: graph capture, symbolic and dynamic shapes, control flow, automatic differentiation
- GPU kernel programming and architecture: Triton, cuTile, CuTeDSL, CUDA, compute and memory hierarchies, programming with tensor cores
- Compiler concepts: static analysis, intermediate representations like SSA and CFGs, instruction selection
- ML inference: CUDA Graphs, KV caching, batching
- ML training: Tensor/Sequence/Expert/Pipeline Parallelism, FSDP, NCCL, activation checkpointing
- GPU debugging and optimization: CUDA-GDB, Nsight Systems, Nsight Compute
- Formal methods: proof assistants, automated theorem provers, agentic proof systems
- Programming languages and DSL design: embedded DSLs, staging and metaprogramming, type systems, functional programming
If you're a recruiting agency and want to partner with us, please reach out to agency-partnerships@janestreet.com
Why work at Jane Street
- Culture: Described as “open and collaborative,” with porous boundaries between trading, research, and technology. Intellectual humility and curiosity are highly valued.
- Learning & Development: Offers immersive internships, multi-day programs, direct mentorship, and a week abroad for interns. Full-time employees have access to extensive internal training, a library, and classrooms in every office.
- Work Environment: Most employees write code as part of their regular work. The firm is known for its technical excellence and for using the most advanced tools available.
- Benefits: Daily breakfast and lunch, fully-stocked kitchens, on-site gyms, nursing suites, quiet rooms, recreation spaces, health services, and more. Offices are designed for collaboration and deep focus.
- Remote/Hybrid Policy: Not explicitly stated, but the firm emphasizes in-person collaboration on trading floors. Offices are located in major global cities.
- Diversity: Actively seeks people from all backgrounds and emphasizes that the company is only as strong as its people.