ML Systems Software Engineer
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Key skills for this role
Role Overview
Work at the boundary between machine-learning models and silicon as part of the ILM team.
Focus on systems software, model serving, accelerator utilization, and measurable performance improvements.
Key Skills for This Role
Full Job Posting
Role overview
Work at the boundary between machine-learning models and silicon as part of the ILM team.
Focus on systems software, model serving, accelerator utilization, and measurable performance improvements.
What you will do
- Build and optimize batching, memory management, cache behavior, and scheduling under real concurrency.
- Profile end to end to identify actual bottlenecks rather than assumed ones.
- Make models share accelerators across silicon with different capacities and generations.
- Work with drivers, kernels, memory allocators, and telemetry close to the runtime.
- Build measurement harnesses and validate results on real hardware.
- Take changes from hypothesis to benchmark to production.
Required qualifications
- Four or more years in systems or ML infrastructure engineering.
- Strong Python and a systems language, typically C++ or Rust.
- Experience optimizing inference or training throughput and explaining the performance gain.
- Understanding of accelerator memory hierarchies and kernel launch behavior.
- Comfort working on bare metal rather than behind a managed service.
Useful but not required
- CUDA, ROCm, Triton, or comparable kernel-level work.
- Contributions to vLLM, TensorRT-LLM, SGLang, or similar projects.
- Distributed serving and multi-accelerator sharding experience.
- Published benchmark or systems work.
What the company offers
Opportunity to work at an early-stage deep-tech company building foundational technology in-house.
Meaningful ownership and impact with a small, senior team.
Problems combining hardware, systems, and AI at scale.
Competitive early-stage compensation.
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