2026 Summer Intern – Machine Learning Systems Engineer – Autonomous Driving (Master's Degree)
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About this role
About the Team:
We are building a state-of-the-art AI training platform in close collaboration with model teams to deliver Autonomous car milestones, maximizing model flop utilization and iteration speed, and enhancing inference systems. We are augmenting training, data processing, evaluation and the deployment loop with a data flywheel to validate performance and continuously improve Autonomous driving and Active Safety. We accelerate compute performance through architectural changes and low-level optimizations, informed by advanced Carbench/HIL platforms, AI tooling and workflows and instrumentation built in-house. We streamline compiler/build and modelbench performance; enhance data processing, preprocessing, and batch inference; and improve monitoring and availability to increase velocity and quality.
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.
To help facilitate administration of the relocation stipend if you are selected, please apply using the permanent address you would move from.
About the Role:
As an intern, you will help develop and optimize our AI training platform by improving data processing pipelines, accelerating model training and inference workflows, and/ or enhancing testing infrastructure like Carbench/HIL. You will support tool development, instrumentation, and system monitoring to boost reliability, reduce latency, and increase iteration speed for advancing 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 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).
- Strong communication skills with experience collaborating across cross-functional teams.
- Able to work fulltime, 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) to enhance productivity and understanding of ML systems.
- Demonstrated creativity and quick problem-solving capabilities.
- Research and/or work experience in a relevant field, such as machine learning, deep learning, reinforcement learning, NLP, recommendation systems, pattern recognition, signal processing, data mining, artificial intelligence, or computer vision.
- Experience with distributed systems (e.g., Spark, Ray, Kubernetes, Slurm).
- Experience in CUDA, OpenCL, Triton or other accelerator programming language.
- Intent to return to degree-program after the completion of the internship.
- Graduating between December 2026 and August 2027.