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
Tools in this posting
- PyTorch
- TensorFlow
Source — Tool mentions in context
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:
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.
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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
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Source details
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- Location & working pattern
New York, New York, United States
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