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

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  Design and build the knowledge distillation pipelines that transfer reasoning, vision, and language capability from foundation models into compact architectures suitable for edge deployment.

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  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.

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  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.

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  Curate, evaluate, and synthetically generate the datasets required to teach smaller models to accurately interpret passenger intent and complex visual cues inside the vehicle.

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  Implement Quantization-Aware Training or similar techniques, adjusting model architectures and training regimes to prevent accuracy degradation when models are compressed for hardware deployment.

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  Establish the evaluation frameworks and benchmarks for fine-tuned models, measuring hallucination rates, domain accuracy, and safety constraints.

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  Own our base model strategy: decide which foundation architectures we build on, and make the case for switching when something better arrives.

• 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 Seattle, WA, reporting to the office three days per week at minimum.

• Master's degree or Ph.D. in Computer Science, Artificial Intelligence, or a related field.

• Experience shipping a quantized model to a specific hardware target, including working through the accuracy regressions that surfaced along the way.

• Familiarity with the broader training ecosystem (Hugging Face, DeepSpeed, Ray, or Megatron) and experience managing dataset pipelines at scale.

• Domain experience in conversational AI, human-computer interaction, smart spaces, or deploying multimodal models in consumer-facing products.

• Open-source contributions to foundation model tuning libraries, or published research on model compression, distillation, or efficient AI.

• Ability to communicate complex AI training concepts and architectural trade-offs to cross-functional product and engineering teams.

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