Pay
$118K–$193K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,721 postings in the last year. Stated ranges, not salaries paid.
Machine Learning Compiler Engineer
New York, New York, United States
Actively hiring, first seen by us today- Pay
- Pay not listedSimilar roles state $118K–$193K
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full-Time: Experienced
From the employer’s posting
What you’ll work on
Full postingShape the development of the ML compiler stack
Turn research ideas and novel architectures into high-throughput training jobs
Evaluate current ML compiler technologies and decide where to build own solutions
From the employer’s posting
In this role, you would shape the development of our compiler stack.
ML compilers let us quickly turn research ideas and novel architectures into high-throughput training jobs and low-latency inference.
we are working to understand and improve the current ML compiler technologies and explore where it makes sense to build our own solutions
What you’ll bring
All qualificationsCore experience
- Experience working on an ML compiler (preferred, but not required)
- Understanding of how ML frameworks, graph compilers and kernel DSLs fit together
Preferred experience
- Familiarity with GPU kernel programming such as Triton, CUDA or CuTeDSL
- Knowledge of compiler concepts such as SSA, CFGs and static analysis
Qualification wording
The ideal candidate has worked on an ML compiler in the past, but non-ML compiler experience is also valuable.
You should understand how all those pieces fit together even if you are unfamiliar with specific parts.
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
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 4, 2026today
- Recorded sightings
- 1
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 0
Early. Similar jobs usually come down between day 7 and day 55.
From 10,019 similar postings that came down. Coming down is not always a hire.
Jane Street is hiring
26 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Hiring has slowed
- Its jobs tend to stay up a long time
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- PyTorch
- TensorFlow
Where the posting names them
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:
Your next step
Complete your application on www.janestreet.com. The employer’s form will show what is required.
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Has Jane Street posted this job before?
Yes. We’ve followed one other Machine Learning Compiler Engineer job there since November 2026. It is still up.
About the role
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
US government figures for data scientists
- Median yearly pay, May 2025
- $120,230
- Expected change, 2025 to 2035
- 34.6% more jobsagainst 3.5% for all jobs
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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
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- Location & working pattern
New York, New York, United States
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