In this role, you will:
- Design, develop, and deploy custom and off-the-shelf ML libraries and toolings to improve ML development, training, deployment, and on-vehicle model inference latency.
- Build tooling and establish development best practices to manage and upgrade foundational libraries, i.e., Nvidia driver, PyTorch, TensorRT, etc., improve ML developer experience, and expedite debugging efforts.
- Collaborate closely with cross-functional teams, including applied ML research, high-performance compute, advanced hardware engineering, and data science, to define requirements and align on architectural decisions.
Qualifications
- 6+ years of experience
- Proficient in Python or C++
- Familiarity with any of the training frameworks and libraries like PyTorch, Lightning, Hugging Face, Ray, JAX, etc.
- Familiarity with any of the GPU-accelerated inference on Nvidia hardware like CUDA, TensorRT, and/or XLA
Bonus Qualifications
- Familiarity with Bazel and/or the C++ linker
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