Job description

Job Description Work Arrangement: Hybrid: This internship is categorized as a hybrid. The selected intern is expected to report to the office up to three times per week or as determined by the team. Location: Sunnyvale, California About the Team: The AV ML Infra team at GM builds ML infrastructure designed to meet the unique demands of AI and ML innovation, supporting a wide range of use cases across teams such as Embodied AI, Simulation, Data Science, and more. We enable scalable and efficient ML experimentation, enhance the productivity of ML engineers, and drive the adoption of cutting-edge ML techniques. Our AV ML infrastructure includes: AI Validation & Inference: Ensures robust model performance by running large-scale simulation workloads and managing reliable ML inference pipelines. ML Compute: Streamlines and optimizes large-scale ML training and inference across cloud and on-prem compute resources. AV Pipelines & Lineage: Automates ML workflows via Orchestration platform while tracking data and model lineage across diverse infrastructures, accelerating engineering velocity and ensuring reproducibility. Together, these tools and systems empower GM to tackle the complexities of autonomous driving technology and expedite our path to commercialization. About the Role: As an intern, you will help develop and optimize our AV ML Infra by improving data processing pipelines, scheduling, accelerating model training and inference workflows, and/orenhancing testing infrastructure like Carbench/HIL. You will support tool development, instrumentation, and system monitoring to boost reliability, reduce latency, and increase iteration speed for autonomous driving performance. What You’ll Do: • Develop scalable infrastructure and tools to support model training, regression, and rules-based models, operations, and inference. • Suggest, collect and synthesize requirements and create effective feature roadmap. • Code deliverables in tandem with the engineering team. • Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU). • Perform specific responsibilities which vary by team. Required Qualifications: • Currently enrolled in a full-time, degree-seeking program and in the process of obtaining 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 (e.g., Java, C , Python). Experience coding in Java, C/C , Perl, PHP, GO or Python. • Strong communication skills with experience collaborating across cross- functional teams. • Able to work full-time, 40 hours per week. Preferred Qualifications: • Demonstrated software engineer experience via an internship, work experience, coding competitions. • Familiarity with AI-assisted engineering tools (e.g., for code generation, model analysis, or experiment planning). • Demonstrated creativity and quick problem-solving capabilities. • Research and/or work experience in machine learning infrastructure. • Experience with Hadoop/Hbase/Pig or Mapreduce/Sawzall/Bigtable. • Experience with distributed systems (e.g., Spark, Ray, Kubernetes, Slurm). • Experience in scheduling algorithms, orchestration or compute. • Intent to return to degree-program after internship. • Graduating between December 2027 and August 2028. Compensation: • The monthly salary range for this role is $9,600 - $11,800 per month. • GM will provide a one-time lump sum taxable stipend payment to eligible students selected for the 2027 Student Program. What You’ll Get from Us (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 About GM Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all. Why Join Us We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team. Benefits Overview From day one, we re looking out for your well-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources . Non-Discrimination and Equal Employment Opportunities (U.S.) General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers. All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws. We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire . Accommodations General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.

Requirements

Must-haves

Hybrid: This internship is categorized as a hybrid. The selected intern is expected to report to the office up to three times per week or as determined by the team.
Sunnyvale, California
AI Validation & Inference: Ensures robust model performance by running large-scale simulation workloads and managing reliable ML inference pipelines.
ML Compute: Streamlines and optimizes large-scale ML training and inference across cloud and on-prem compute resources.
AV Pipelines & Lineage: Automates ML workflows via Orchestration platform while tracking data and model lineage across diverse infrastructures, accelerating engineering velocity and ensuring reproducibility.
Together, these tools and systems empower GM to tackle the complexities of autonomous driving technology and expedite our path to commercialization.
Develop scalable infrastructure and tools to support model training,
regression, and rules-based models, operations, and inference.
Suggest, collect and synthesize requirements and create effective feature
Code deliverables in tandem with the engineering team.
Adapt standard machine learning methods to best exploit modern parallel
environments (e.g. distributed clusters, multicore SMP, and GPU).
Perform specific responsibilities which vary by team.

Nice-to-haves

Demonstrated software engineer experience via an internship, work
experience, coding competitions.
Familiarity with AI-assisted engineering tools (e.g., for code generation,
model analysis, or experiment planning).
Demonstrated creativity and quick problem-solving capabilities.
Research and/or work experience in machine learning infrastructure.
Experience with Hadoop/Hbase/Pig or Mapreduce/Sawzall/Bigtable.
Experience with distributed systems (e.g., Spark, Ray, Kubernetes, Slurm).
Experience in scheduling algorithms, orchestration or compute.
Intent to return to degree-program after internship.
Graduating between December 2027 and August 2028.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlaimachine learningjavapythongokubernetes

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Vision