Job DescriptionYou will work on a custom ML compiler that transforms modern ML and DSP models into highly efficient programs for our accelerator.
This role spans the full compiler stack-from ingesting models and transforming intermediate representations to optimizing execution under tight memory and latency constraints.
What You'll Do- Build and maintain model ingestion pipelines (e.g., PyTorch / ONNX 12 internal IR)
- Implement graph transformations such as:
- Operator decomposition and canonicalization
- Shape inference and layout transformations
- Develop and extend intermediate representations (e.g., MLIR)
- Implement optimization passes including:
- Operator fusion and graph partitioning
- Basic scheduling and tiling strategies
- Memory planning and reuse
- Debug correctness and numerical issues across transformations
- Collaborate with hardware and ML teams to improve system performance
RequirementsRequired qualifications and experience:
- 2+ years of experience in compilers and/or edge-AI
- Proficiency in Python and/or C++
- Experience with at least one:
- MLIR, LLVM, TVM, XLA, or similar
- Graph-level transformations or ML model internals
- Understanding of deep learning models (conv, sequence models, etc.)
- Ability to reason about correctness and performance tradeoffs
Nice to have:
- Experience with optimization techniques (tiling, scheduling, memory reuse)
- Familiarity with ONNX or PyTorch internals
- Exposure to quantization or low-precision computation
- Interest in hardware-aware ML systems
Benefits- 401(k)
- Medical insurance
- Vision insurance
- Dental insurance
- Commuter benefits
- Disability insurance
- Paid maternity leave
- Paid paternity leave
- Child care support
