Staff AI/ML Software Engineer, Model Distillation & Fine-Tuning

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About the role

Job Description Work Arrangement: This role is categorized as hybrid. This means the successful candidate is expected to report to Mountain View, CA three times per week at minimum or other frequency dictated by the business. The Role General Motors is bringing multimodal AI into the vehicle, and we are looking for a Staff AI/ML Software Engineer to lead the adaptation, fine-tuning, and distillation of foundation models for the automotive edge. You will build models that understand driver intent, conversational context, passenger requests, and the visual state of the cabin.   Large, general-purpose vision-language models (VLMs) and LLMs are highly capable, but their size makes them impractical to run on constrained vehicle compute. Slicing them down naively degrades exactly the reasoning and multimodal ability that made them worth deploying. Solving that is the core of this job.   You will join Vehicle Applied AI, the team that identifies, validates, and de-risks the AI capabilities that will define our future vehicles. We prove feasibility on representative vehicle hardware and chart a practical path to scale.   As an individual contributor technical leader, you will set the architectural direction for our model optimization pipelines. You will take the lead on parameter-efficient fine-tuning, dataset curation for complex human-machine interaction use cases, and teacher-student knowledge distillation. You will connect foundation model research with practical deployment, ensuring your models understand the cabin environment, improve through continuous data loops, and perform reliably after edge quantization. If you are a strong ML practitioner focused on maximizing the "intelligence per parameter" of compact models, this is the role for you.   What You'll Do • Design and build the knowledge distillation pipelines that transfer reasoning, vision, and language capability from foundation models into compact architectures suitable for edge deployment. • Apply and scale parameter-efficient fine-tuning techniques (LoRA, QLoRA, or similar) to adapt general-purpose models to specific cabin interaction and conversational AI use cases. • Build and own the reinforcement learning flywheel, implementing human-in-the-loop alignment (RLHF/DPO) and closing the loop between in-cabin data collection and continuous model improvement. • Curate, evaluate, and synthetically generate the datasets required to teach smaller models to accurately interpret passenger intent and complex visual cues inside the vehicle. • Implement Quantization-Aware Training or similar techniques, adjusting model architectures and training regimes to prevent accuracy degradation when models are compressed for hardware deployment. • Establish the evaluation frameworks and benchmarks for fine-tuned models, measuring hallucination rates, domain accuracy, and safety constraints. • Own our base model strategy: decide which foundation architectures we build on, and make the case for switching when something better arrives. Your Skills & Abilities (Required Qualifications) • Bachelor's degree in Computer Science, Machine Learning, Data Science, Mathematics, or equivalent practical experience. • 8+ years of software engineering or applied ML research experience, including work where you set the technical direction others built against, made the architectural calls on an ML system, and brought other engineers along with you. • Deep proficiency in PyTorch. • Hands-on experience fine-tuning large language models or vision-language models, with results you can speak to in detail. • Practical experience with at least two of: knowledge distillation, parameter-efficient fine-tuning, pruning, or quantization. • Based in or willing to work hybrid out of Mountain View, CA or Seat

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