Machine Learning Engineering Intern

Bonsai Robotics develops affordable, vision-based autonomy that makes off-road equipment smarter, safer, and more productive. We are redefining outdoor autonomy with Bonsai Intelligence, a connected platform that's inspired by biology to see, think, and act with precision like a human. We bring together advanced perception, embodied AI, integrations with equipment manufacturers, and our compact, modular Amiga vehicles to deliver reliable automation to the world's most demanding field operations—reducing costs and increasing operational efficiencies.

We're looking for an ML Engineering Intern to support our perception and learned-behavior teams. You'll work on real problems with real data from machines operating in the field—not toy datasets. Depending on your interests and our team's priorities, you might work on projects like:

• Building auto-labeling pipelines to scale our training data
• Optimizing model inference for edge deployment (NVIDIA Jetson)
• Supporting BEV (bird's-eye-view) perception or VLA model training and evaluation

This is a hands‑on role where you'll ship code that impacts our autonomy stack.

• Pursuing a BS, MS, or PhD in CS, EE, Robotics, or a related field
• Solid fundamentals in deep learning and computer vision (coursework or projects)
• Proficiency in Python and PyTorch (or similar frameworks)
• Comfortable writing clean, functional code—not just notebooks
• Eager to learn, self‑directed, and excited about robotics

• Experience with 3D vision, LiDAR processing, or sensor fusion
• Familiarity with model optimization (Tensor
  
  RT, ONNX) or edge deployment
• Exposure to generative models, simulation, or synthetic data
• Experience with ROS2 or robotics middleware

You'll work alongside experienced ML and robotics engineers on models that drive real machines in real fields. If you want an internship where your work hits production—not a slide deck, we'd love to hear from you.

Bonsai Robotics is an Equal Employment Opportunity employer and considers all qualified applicants without regard to race, color, religion, sex, sexual orientation, national origin, ancestry, age, disability, gender identity or expression, marital status, or any other legally protected status.