12 days ago
Base Salary
$190k - $250k/yr
Responsibilities
- Maintain, update, and optimize JAX, PyTorch, CUDA, and ROCm across diverse environments and hardware configurations.
- Own and improve the end-to-end ML software stack from ROCm/CUDA drivers to high-level framework tooling.
- Optimize model sharding, partitioning, and configuration for large-scale distributed training and serving.
- Integrate and validate modules for runtime correctness, memory efficiency, and scalability across multi-node GPU and accelerator clusters.
- Profile compilation graphs, training workloads, and runtime execution to identify and eliminate performance bottlenecks.
- Debug hardware-software interaction issues, including vLLM compilation failures on ROCm, CUDA memory leaks, distributed runtime failures, and kernel inconsistencies.
- Collaborate with research, infrastructure, and kernel engineering teams to improve throughput, stability, and developer experience.
Requirements
- 5+ years of industry experience in ML systems, distributed training, or related fields.
- Bachelor’s or master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
- Strong programming experience in Python and C++, with familiarity with ML tooling and distributed systems.
- Deep understanding of profiling tools such as Nsight, ROCm Profiler, XLA profiler, and TPU tools.
- Expertise configuring partitioning in modern ML frameworks including PyTorch and JAX.
- Experience with multi-node distributed training systems and orchestration frameworks such as DTensor and GSPMD.
- Hands-on experience maintaining or building ML training stacks involving CUDA, ROCm, NCCL, XLA, or similar technologies.
- Preferred: extensive XLA/JAX experience, including compilation internals and custom lowering paths.
- Preferred: familiarity with distributed serving or large-scale inference frameworks such as vLLM, TensorRT, and FasterTransformer.
- Preferred: GPU kernel optimization, accelerator-aware model partitioning, and low-level C++ building blocks used in ML frameworks.
Benefits
- Medical, dental, and vision insurance.
- 401k plan.
- Daily lunch, snacks, and beverages.
- Flexible time off.
- Competitive salary and equity.
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