2026 Summer Intern - Machine Learning Engineer- Autonomous Vehicle Engineering (PhD)
Skip the busywork
ApplyBolt rewrites your resume for this exact role and hits submit. You just pick the jobs.
About this role
About the Team:
Offboard perception: Unlike onboard perception, which must run in real time on the AV, offboard perception can leverage larger models, greater compute, and acausal processing to achieve much higher accuracy. These high-accuracy detections support multiple consumers across the organization—for example, powering simulation and generating automated labels used to train onboard models.
About the Role:
As a Machine Learning Engineering Intern, you will work alongside a world-class team of engineers and researchers on impactful AI/ML projects in robotics and advanced manufacturing. You’ll gain hands-on experience contributing to the design, prototyping, and potential deployment of AI systems in real-world industrial settings. This is an excellent opportunity to explore applied research and development while learning from top experts in the field.
What You’ll Do:
- Collaborate with cross-functional teams to adapt and optimize machine learning models for autonomous vehicle perception, including robotics and computer vision applications.
- Help build and test components of end-to-end deep learning pipelines that process multimodal sensor data (e.g., cameras, lidars, radars).
- Assist in developing and evaluating foundation models and transfer learning techniques for use
- Participate in translating technical and business requirements into ML prototypes.
- Gain exposure to the training, deployment, and performance monitoring of machine learning models in production-like environments.
- Learn how research ideas evolve into real-world applications and contribute to the team's innovation roadmap.
Required Qualifications:
- Currently enrolled in a PhD program in Computer Science, Machine Learning, Robotics, or a related STEM field.
- Availability to work full-time (40 hours per week) during the internship period.
- Demonstrated coursework, research., or projects in AI/ML.
- Strong programming skills in Python.
- Able to work fulltime, 40 hours per week.
Preferred Qualifications:
- Exposure to deep learning architectures such as Transformers, CNNs, or Diffusion Models.
- Hands-on experience with one or more machine learning frameworks (e.g., PyTorch, TensorFlow, JAX, or Keras).
- Experience with robotics, computer vision through projects or research.
- Familiarity with multimodal learning or working with sensor data.
- Interest in contributing to publications, open-source projects, or patents.
- Familiarity with systems programming languages (e.g., C++ or Java) is a plus.
- Intent to return to degree-program after the completion of the internship.
- Graduating between December 2026 and June 2027.