AI/ML Engineer, Model Inference

About the Role

A hands-on machine learning engineer is sought to contribute to the data processing, featurization, and inference foundations that enable scalable world understanding. This role is suited for individuals proficient in machine learning systems, production infrastructure, and evaluation loops, who excel at transforming ambiguous challenges into practical, reliable solutions.

Key Responsibilities

• Design, build, and productionize data processing and featurization pipelines for large-scale multimodal data.
• Enhance inference frameworks for computer vision and multimodal models, prioritizing reliability, extensibility, and operational simplicity.
• Drive scalability and cost efficiency across the end-to-end pipeline, including compute utilization, throughput, storage, and query performance.
• Collaborate with partners across machine learning, infrastructure, and evaluation to deliver systems supporting both rapid experimentation and production use.
• Develop and refine evaluation methods for model quality, retrieval quality, and system-level performance.
• Shape technical direction through strong execution, thoughtful tradeoff analysis, and clear engineering judgment.
• Take ownership of ambiguous problem spaces, define practical paths forward, and transition prototypes to production rapidly.
• Operate with urgency and a strong bias toward execution velocity, while upholding a high standard for engineering quality.

Requirements & Tech Stack

• Computer Science, Electrical Engineering, Robotics, or related technical field (BS, MS, or PhD) or equivalent practical experience.
• Experience building production data processing or machine learning pipelines at scale.
• Experience with featurization, embedding, inference, or retrieval systems for vision or multimodal workloads.
• Strong understanding of computer vision models and the practical challenges of deploying them in production.
• Experience evaluating machine learning systems using clear metrics, experiments, and regression safeguards.
• Proven ability to work hands-on in fast-moving environments with incomplete information.
• Strong ownership mindset, sound technical judgment, and the ability to drive execution through ambiguity.

Competitive Edge

• Experience with world models or large-scale world understanding systems.
• Experience with simulation workflows or synthetic data systems.
• Experience with vector search, approximate nearest neighbor retrieval, or large-scale embedding infrastructure.
• Experience working on embodied AI, autonomous systems, or safety-critical machine learning applications.

Compensation & Benefits

• Salary Range: $117,700 - $221,400
• Bonus Potential: Incentive pay program based on company, job level, and individual performance.
• Benefits: Health and wellbeing programs including medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, and GM vehicle discounts.

Work Location

• This role is based remotely.
• If the selected candidate lives within a specific radius of a GM hub, they will be expected to report to that location three times a week or as dictated by their manager.
• Relocation benefits are available for qualifying candidates.