Machine Learning Compiler Engineer
Janestreet has an opening for a Machine Learning Compiler Engineer in London, United Kingdom. It is listed as a full-time position.
DGJ summary of this job
Our own short read of this listing, built only from the facts it gives. The employer’s original text follows further down.
Janestreet has an opening for a Machine Learning Compiler Engineer in London, United Kingdom. It is listed as a full-time position.
Reading the advert, the employer asks for English language skills.
Skills and tools named in the text include PyTorch and TensorFlow.
No salary is given in this listing, so check the official pay figures in the DGJ Insight section below where they exist.
The advert does not mention visa sponsorship. If you would move to United Kingdom for this job, check the official work permit route first (DGJ Insight lists it where we have it).
Dev Global Jobs found this job on Arbeitnow on Nov 4, 2026.
- Employer
- Janestreet
- Location
- London, United Kingdom
- Job type
- Full-time
- Pay
- Not stated by the employer
- Languages asked
- English
- Found
- Nov 4, 2026 on Arbeitnow
- Employer text
- 405 words, about 2 min to read
- Apply only on the official page (arbeitnow.co.uk). Dev Global Jobs is not the employer and never asks for a fee.
- Mirror the skills the advert names (PyTorch and TensorFlow) in your CV where you really have them; applicant tracking systems look for these words.
- Say your level in English plainly (for example B2 or C1).
- Read the employer’s full text below (about 2 min) before you apply.
- Build or check your CV with the free DGJ CV Builder.
DGJ Insight
Our own data about this role and place, from official statistics and our live job index.
Source: OECD Average annual wages, 2025
UN living-cost index for United Kingdom (ICSC post adjustment 76.4), as of 1 Oct 2026.
Source: ICSC post adjustment classifications (icsc.un.org)
- Skilled Worker visa: Job offer from a licensed sponsor at the required skill and salary level; English requirement
- Health and Care Worker visa: Eligible health and social care roles with a licensed sponsor
- Global Talent visa: Endorsement as a leader or potential leader in a listed field
Official routes: UK Visas and Immigration (GOV.UK)
About the role
Full text from the employer, as published on Arbeitnow, arranged into sections. Nothing added or removed.
01About 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.
02About 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
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