2027 Intern - AI/ML Engineer, Autonomous Vehicle ML Infrastructure

GM • Sunnyvale, CA

24d • Hybrid

About The Position

The AV ML Infra team at GM builds machine-learning infrastructure to support AI and ML use cases across teams such as Embodied AI, Simulation, and Data Science. The infrastructure supports scalable experimentation, improves ML engineering productivity, and advances the adoption of ML techniques. It includes AI validation and inference for large-scale simulation workloads and reliable inference pipelines; ML compute for training and inference across cloud and on-premises resources; and AV pipelines and lineage for workflow orchestration and tracking data and model lineage.

As an intern, you will help develop and optimize AV ML infrastructure by improving data-processing pipelines and scheduling, accelerating model-training and inference workflows, and/or enhancing testing infrastructure such as Carbench/HIL. You will contribute to tool development, instrumentation, and system monitoring to improve reliability, reduce latency, and increase iteration speed for autonomous-driving performance. You will develop infrastructure and tools for model training, regression, rules-based models, operations, and inference; gather and synthesize requirements; help create feature roadmaps; and deliver code alongside the engineering team. You may adapt machine-learning methods for parallel environments, including distributed clusters, multicore SMP, and GPUs. Specific responsibilities vary by team.

Requirements

• Currently enrolled in a full-time, degree-seeking program and pursuing a bachelor's or master's degree in computer science or a related technical field.
• Experience in systems software or algorithms.
• Experience with modern object-oriented programming languages such as Java, C++, or Python; coding experience in Java, C/C++, Perl, PHP, Go, or Python.
• Strong communication skills and experience collaborating across cross-functional teams.
• Able to work full-time, 40 hours per week.

Nice To Haves

• Demonstrated software-engineering experience through an internship, work experience, or coding competitions.
• Familiarity with AI-assisted engineering tools for code generation, model analysis, or experiment planning.
• Creativity and quick problem-solving capabilities.
• Research and/or work experience in machine-learning infrastructure.
• Experience with Hadoop, HBase, Pig, MapReduce, Sawzall, or Bigtable.
• Experience with distributed systems such as Spark, Ray, Kubernetes, or Slurm.
• Experience in scheduling algorithms, orchestration, or compute.
• Intent to return to a degree program after the internship.
• Graduating between December 2027 and August 2028.

Responsibilities

• Develop scalable infrastructure and tools for model training, regression, rules-based models, operations, and inference.
• Suggest, collect, and synthesize requirements and create an effective feature roadmap.
• Code deliverables in tandem with the engineering team.
• Adapt standard machine-learning methods to exploit modern parallel environments such as distributed clusters, multicore SMP, and GPUs.
• Support improvements to data-processing pipelines, scheduling, model-training and inference workflows, and testing infrastructure such as Carbench/HIL.
• Develop tools, instrumentation, and system monitoring to improve reliability, reduce latency, and increase iteration speed.
• Perform team-specific responsibilities.

Benefits

• Paid US GM holidays.
• GM Family First Vehicle Discount Program.
• Result-based potential for growth within GM.
• Intern events to network with company leaders and peers.
• One-time lump-sum taxable stipend payment for eligible students selected for the 2027 Student Program.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern