Machine Learning Compiler Engineer
Jane Street12 hours ago
Responsibilities
- Shape the development of Jane Street’s ML compiler stack.
- Improve compiler technologies to support high-throughput training and low-latency inference.
- Work on compiler optimization, graph capture, symbolic and dynamic shapes, control flow, and automatic differentiation.
- Develop or optimize GPU kernels and leverage GPU architectures, tensor cores, and compute and memory hierarchies.
- Support ML inference and training systems, including batching, caching, parallelism, checkpointing, and communication.
- Investigate compiler concepts, formal methods, programming languages, and domain-specific language design.
Requirements
- Experience working on an ML compiler is ideal, while non-ML compiler experience is also valuable.
- Understanding of ML compiler stacks, including high-level frameworks, graph compilers, and kernel DSLs.
- Interest or experience in algebraic rewriting, fusion, scheduling, loop-level optimization, autotuning, or related compiler optimizations.
- Knowledge of compiler concepts such as static analysis, intermediate representations, instruction selection, SSA, or CFGs.
- Experience or interest in GPU kernel programming, CUDA, tensor cores, GPU debugging, and performance optimization.
- Familiarity with ML inference, ML training parallelism, formal methods, programming languages, or DSL design is valuable.
Tech Stack
PyTorchTensorFlow
Categories
About Jane Street
Jane Street is a global quantitative trading firm and liquidity provider that builds in-house software and research platforms to trade across asset classes. It makes markets and executes proprietary strategies on exchanges and electronic venues, serving institutional markets rather than individual investors. Founded in 2000 and headquartered in New York, it is privately held with offices in London, Hong Kong, Singapore, and Amsterdam.