AI/ML Engineer – Model Inference

General Motors

. Design, build, and productionize data processing and featurization pipelines for large-scale multimodal data .

Posted 9/18/2026full-timeSunnyvale • California • United StatesMid-LevelSenior💰 $117,700 - $221,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building data processing and featurization pipelines for large-scale multimodal data, with a strong focus on computer vision models and evaluation methods. Proven ability to drive execution and scalability in fast-paced environments while maintaining engineering quality.

Highest-signal resume keywords

Data Processing Pipeline DevelopmentComputer Vision Model DeploymentMachine Learning Evaluation MethodsScalability and Cost EfficiencyOwnership and Execution in Ambiguity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ProcessingFeaturizationEmbedding SystemsInference FrameworksRetrieval SystemsMachine Learning PipelinesComputer VisionEvaluation MetricsVector SearchLarge-Scale Embedding Infrastructure

Soft Skills

Technical JudgmentCollaborationProblem-SolvingAdaptabilityOwnership Mindset

Tools & Technologies

Simulation WorkflowsSynthetic Data SystemsWorld ModelsAutonomous SystemsSafety-Critical Machine Learning

Industry Keywords

Multimodal DataMachine LearningData FeaturizationModel QualityThroughputQuery Performance

About the role

Key responsibilities & impact

Design, build, and productionize data processing and featurization pipelines for large-scale multimodal data
Improve inference frameworks for computer vision and multimodal models
Drive scalability and cost efficiency across compute utilization, throughput, storage, and query performance
Collaborate with machine learning, infrastructure, and evaluation partners
Develop and refine evaluation methods for model quality, retrieval quality, and system-level performance
Shape technical direction through execution, tradeoff analysis, and engineering judgment
Take ownership of ambiguous problem spaces and move prototypes to production
Maintain execution velocity and engineering quality

Requirements

What you’ll need

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
Strong ownership mindset, sound technical judgment, and ability to drive execution through ambiguity
Experience with world models or large-scale world understanding systems is a competitive edge
Experience with simulation workflows or synthetic data systems is a competitive edge
Experience with vector search, approximate nearest neighbor retrieval, or large-scale embedding infrastructure is a competitive edge
Experience working on embodied AI, autonomous systems, or safety-critical machine learning applications is a competitive edge
Applicants may be required to complete role-related assessments and/or pre-employment screening

Benefits

Comp & perks

Medical insurance
Dental insurance
Vision insurance
Health Savings Account
Flexible Spending Accounts
Retirement savings plan
Sickness and accident benefits
Life insurance
Paid vacation and holidays
Tuition assistance programs
Employee assistance program
GM vehicle discounts
Bonus potential based on company, job level, and individual performance
Remote work arrangement
Relocation benefits for candidates who qualify under company policy