Senior Machine Learning Engineer - LLM Quantization & Deployment

Develop VLA inference models, ensure numerical consistency with training models, and productionize LLM quantization methods, including PTQ, QAT, mixed-precision inference, INT8, FP4, and lower-bit techniques.

Develop production-quality Python code with strong testing, observability, reproducibility, and failure handling.

Build robust model export, calibration, benchmarking, validation, and deployment pipelines.

Engage early with the VLA model research team to establish performance estimates and prove model feasibility.

Curate evaluation datasets and establish a comprehensive metric suite to systematically benchmark VLA performance.

Analyze numerical errors, accuracy regressions, and performance trade-offs.

Develop PTQ and QAT orchestration workflows.

Serve as the primary interface with field-testing and simulation teams for issue triage and autonomous driving performance sign-off.

Collaborate with the in-vehicle software team on latency analysis and issue triage.

Collaborate with the training infrastructure team to develop QAT and model distillation.

Master in CS/CE/EE, or equivalent, with 1-3 years of industry experience. Open to new graduates.

Strong understanding of Transformer architectures and LLM inference.

Hands-on experience quantizing or deploying deep learning models in production.

Proficiency with PyTorch and at least one inference or compilation stack.

Strong Python programming and software engineering skills.

Ability to work effectively across research, systems, infrastructure, and product teams.

Excellent communication and problem-solving skills, with the ability to thrive in a fast-paced and collaborative environment.

Experience with weight-only, activation, KV-cache, dynamic, static, or mixed-precision quantization.

Experience with AWQ, GPTQ, SmoothQuant, or related methods.

Strong numerical analysis and systems engineering skills.

Experience with one or more LLM runtimes, such as TensorRT-LLM, vLLM, SGLang, llama.cpp, ONNX Runtime, TVM, MLIR, or custom runtimes.

Experience deploying LLMs on resource-constrained or heterogeneous hardware.

Contributions to model optimization, inference, compiler, or serving projects.

Publications at NeurIPS, ICML, ICLR, ACL, or related conferences.

A fun, supportive and engaging environment.

Infrastructures and computational resources to support your work.

Opportunity to work on cutting edge technologies with the top talents in the field.

Opportunity to make a significant impact on the transportation revolution by the means of advancing autonomous driving.

Competitive compensation package.

Snacks, lunches, dinners, and fun activities.