AI/ML Engineer - Model Inference

About the role

The Team Cola is part of GM’s autonomous vehicle effort, focusing on data processing, featurization, and inference for scalable world understanding. This role involves designing, building, and productionizing data processing and featurization pipelines for large-scale multimodal data, improving inference frameworks, and developing evaluation methods.

Responsibilities

• Design, build, and productionize data processing and featurization pipelines for large-scale multimodal data
• Improve inference frameworks for computer vision and multimodal models, with a focus on reliability, extensibility, and operational simplicity
• Drive scalability and cost efficiency across the end-to-end pipeline, including compute utilization, throughput, storage, and query performance
• Work closely with partners across machine learning, infrastructure, and evaluation to deliver systems that support 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 move quickly from prototype to production
• Operate with urgency and a strong bias toward execution velocity while maintaining a high bar for engineering quality

Requirements

• BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related technical field, 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 environments
• 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
• A strong ownership mindset, sound technical judgment, and the ability to drive execution through ambiguity

Qualifications

• BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related technical field, 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 environments
• 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
• A strong ownership mindset, sound technical judgment, and the ability to drive execution through ambiguity

Skills & Abilities

• 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

Benefits

• Compensation range: $117,700 - $221,400
• Bonus potential: An incentive pay program based on company performance, job level, and individual performance
• Benefits: 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, GM vehicle discounts

Pay

The salary range for this role is $117,700 and $221,400. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position (along with level.)

Schedule

This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}.

Location

This role is remote, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}.