Jane Street

Trades stocks, bonds and funds as a market maker

Machine Learning Compiler Engineer

Systems Software EngineeringConfirmed live in the last 24 hours

Full-timeLondon, UKNo salary listedIn-person

About the Job

Requirements

Experience working on an ML compiler is ideal; non-ML compiler experience is also valuable.
Understanding of how the ML compiler stack components fit together, including high-level ML frameworks, graph compilers, and kernel domain-specific languages.
Fluency in English is required.

Responsibilities

Help understand and improve current machine-learning compiler technologies and explore where to build proprietary solutions.
Shape the development of the compiler stack.

Desired Qualifications

Experience with high-level ML frameworks such as PyTorch, JAX, or TensorFlow; graph compilers such as Inductor, XLA, or TVM; or kernel domain-specific languages such as Triton, Helion, or cuTile.
Knowledge of machine-learning compiler optimisations, including algebraic rewriting, materialise-versus-recompute tradeoffs, fusion, equality saturation, scheduling languages, loop-level optimisation, and autotuning.
Knowledge of machine-learning compiler frontends, including graph capture, symbolic and dynamic shapes, control flow, and automatic differentiation.
GPU kernel programming and architecture knowledge, including Triton, cuTile, CuTeDSL, CUDA, compute and memory hierarchies, and programming with tensor cores.
Compiler concepts including static analysis, intermediate representations such as SSA and CFGs, and instruction selection.
Machine-learning inference knowledge, including CUDA Graphs, KV caching, and batching.
Machine-learning training knowledge, including tensor, sequence, expert, and pipeline parallelism, FSDP, NCCL, and activation checkpointing.
GPU debugging and optimisation experience using CUDA-GDB, Nsight Systems, and Nsight Compute.
Knowledge of formal methods, including proof assistants, automated theorem provers, or agentic proof systems.
Programming-language and domain-specific-language design knowledge, including embedded domain-specific languages, staging and metaprogramming, type systems, and functional programming.

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:

Optimisations in ML compilers: algebraic rewriting, materialise vs recompute tradeoffs and fusion, equality saturation, scheduling languages, loop-level optimisation, 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 optimisation: 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
Fluency in English is required

If you're a recruiting agency and want to partner with us, please reach out to [email protected].

About the Company

Jane Street

Trades stocks, bonds and funds as a market maker

Jane Street trades stocks, bonds, options, and exchange-traded funds with its own money as a market maker, a firm that constantly quotes prices to buy and sell so other investors can trade whenever they want. In 2025, it made more money from trading than JPMorgan Chase or Goldman Sachs, the biggest trading banks on Wall Street. Its traders, researchers, and engineers build computer-driven strategies that trade on exchanges and other venues around the world, and it also trades directly with large institutional clients. It earns the small gap between buying and selling prices across huge volumes, plus gains on positions it holds for minutes to days. Jane Street was founded in 2000 and is based in New York.

Company size: 1,001-5,000
Company stage: Debt Financing
Total funding: $90.8B
Headquarters: New York City, New York
Founded: 2000

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