Key Responsibilities

• 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.

Basic Qualifications

• Master in CS/CE/EE, or equivalent, with 3-5 years of industry experience.
• 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.

Preferred Qualifications

• 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.

What We Provide

• 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.