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

generalmotors; Mountain View (CA); Hybrid; USD 180,000 - 280,000; Full time

Job summary

General Motors is seeking a Staff AI/ML Software Engineer to lead the adaptation, fine-tuning, and distillation of foundation models for automotive edge deployment. You will build models that understand driver intent, conversational context, passenger requests, and the visual state of the cabin, enabling safer and more intuitive vehicle interactions.

The role combines technical leadership with hands-on development within the Vehicle Applied AI team, aligned to hybrid work in Mountain View or

Responsibilities

• Lead the adaptation, fine-tuning, and distillation of foundation models for the automotive edge.
• Build models that understand driver intent, conversational context, passenger requests, and the visual state of the cabin.
• Set the architectural direction for our model optimization pipelines.
• Lead on parameter-efficient fine-tuning, dataset curation for complex human-machine interaction use cases.

Job description

Job Description

Work Arrangement:

This role is categorized as hybrid. This means the successful candidate is expected to report to Mountain View, CA or Seattle, WA 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,