Job Details

Job Description

To help facilitate administration of relocation benefits if you are selected, please apply using the permanent address you would move from.

Work Arrangement

Hybrid: This internship is categorized as 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 Autonomous Vehicle (AV) Architecture & System Design organization plays a critical role in designing and delivering the foundational architecture for active safety, assisted driving, hands-off, and eyes-off driving across GM's product lines. Our team:

• Owns end-to-end system and sensing requirements for Autonomy features
  

• Defines system architecture spanning compute, sensing, networking, and software integration
  

• Delivers both development and retail fleet data collection to support AV system development and large-scale validation across vehicle programs
  

We work closely with autonomy software, vehicle integration, data, and validation teams to bring safe, scalable autonomous capabilities to GM customers.

About the Role

As a 2026 Summer Intern (PhD) on the AV Architecture & System Design team, you will help develop a Synthetic Novel Architecture Prototyping & Simulation Environment that accelerates exploration of future AV system architectures.

You will help:

• Evolve the framework that models end-to-end AV system architectures (sensing, compute, and data flows)
  

• Update and adapt the AV software stack to run on these new architectures in simulation
  

• Use our system evaluation framework to analyze detailed trade-offs between alternative system architectures, product requirements, and the cost and complexity of vehicle integration
  

What You'll Do

• Contribute to the Synthetic Novel Architecture Prototyping & Simulation Environment, extending models of sensing, compute, and networking for future AV products.
  

• Integrate and adapt components of the AV stack to operate within new system architecture configurations in simulation.
  

• Use our system evaluation framework and data pipelines to quantify trade-offs between architectures along axes such as safety, performance, robustness, and scalability.
  

• Analyze cost and integration complexity impacts of different architecture choices across multiple GM vehicle programs.
  

• Prototype and evaluate novel AV system architectures, from high-level concepts down to concrete, testable configurations.
  

• Collaborate with autonomy software, vehicle integration, data, and validation teams to align architecture concepts with real-world constraints.
  

• Communicate findings and recommendations through clear technical documentation, presentations, and design discussions.
  

Required Qualifications

• Currently enrolled in a PhD program in Computer Science, Electrical/Computer Engineering, Robotics, or a related STEM field.
  

• Solid understanding of modern, state-of-the-art autonomy/AV system architectures, including perception, prediction, planning, and control pipelines.
  

• Very strong Python and C++ programming skills, with experience developing and debugging production-quality code.
  

• Experience with data pipelines (e.g., building or using data tooling for large-scale logs or experiments) and training machine learning models (e.g., perception, representation learning, or decision-making).
  

• Ability to reason about systems-level trade-offs (latency, bandwidth, reliability, safety margins, compute/memory footprint).
  

• Strong problem-solving skills and ability to work independently in a fast-paced environment.
  

• Strong communication skills and a collaborative mindset.
  

• Able to work full-time (40 hours per week) during the internship period.
  

Preferred Qualifications

• Research or project experience in autonomous vehicles, advanced driver assistance systems (ADAS), or robotics.
  

• Experience with simulation environments for AV/robotics (e.g., sensor simulation, HIL/SIL, scenario-based testing).
  

• Familiarity with systems architecture concepts (e.g., distributed systems, real-time systems, safety-critical software, middleware).
  

• Experience working with large-scale datasets and experimentation frameworks for evaluating ML or AV system performance.
  

• Experience with ROS/ROS2 or similar robotics middleware is a plus.
  

• Demonstrated ability to ramp up quickly on complex codebases and ship impactful changes with limited iteration.
  

• Intent to return to your degree program after the completion of the internship.
  

• Target graduation date between December 2026 and June 2028 (or similar timeframe aligned with continuation of PhD studies).
  

Compensation

• The monthly salary range for this role is $13,100 per month.
  

• GM will provide a one-time lump sum taxable stipend payment to eligible students selected for the 2026 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.)

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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.

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