Quant jobs / Jane Street

Machine Learning Compiler Engineer

A compiler engineering role on a new team that builds the compiler stack behind Jane Street's machine learning training and inference. It supports research by turning new model ideas into fast GPU jobs.

What you’d work on

Our reading of the posting: what a new hire in this role would most likely spend their first months on.

Improve how model graphs are captured and optimised for GPUs
Work on kernel-level and graph-level performance for training and inference
Evaluate existing compiler tools against building in-house alternatives
Help researchers run new architectures at speed

What they ask for

Required

ML compiler experience, or other compiler experience
Understanding of ML frameworks such as PyTorch or JAX
Familiarity with graph compilers such as XLA or TVM
Knowledge of GPU kernel programming
Compiler fundamentals such as SSA and intermediate representations
English fluency

Preferred

Distributed training techniques
GPU profiling and debugging
Formal methods
Programming language and DSL design

Education: No degree level stated.
Experience: Prior compiler work, ideally on ML compilers
Languages and tools: Python
CUDA
Triton
PyTorch
JAX

How to prepare

Read how torch.compile or XLA lowers a model into kernels
Write and benchmark a simple Triton or CUDA kernel
Learn core compiler ideas: intermediate representations, static analysis, loop optimisation
Build a toy graph optimiser that fuses operations

What to study

Apply

Read the full posting on Jane Street’s site before applying: requirements, location and pay can change after we check them.

View the posting on Jane Street’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Jane Street and doesn’t handle applications.