Bot Auto
Intern, Deep Learning Engineer
About this role
Bot Auto seeks Master's or Ph.D. candidates to join their AI team as Deep Learning Engineering interns for 3-6 months, working on cutting-edge autonomous trucking projects. You'll prototype state-of-the-art architectures, own targeted research initiatives, and optimize models using large-scale compute clusters and real-world truck datasets.
What you'll do
- Implement and benchmark next-generation deep learning architectures for autonomous driving applications
- Own a research project end-to-end from data analysis through model verification with senior guidance
- Train, tune, and optimize models using large-scale compute clusters
- Work on multi-modal perception, online mapping, behavior prediction, and world models
- Collaborate with the core AI team on real-world edge cases in autonomous trucking
What they're looking for
- Python and PyTorch
- Transformers and modern AI architectures
- Deep Learning and Computer Vision
- Linux and Git
- C++ (preferred)
- Model deployment tools like TensorRT/ONNX (preferred)
- Multi-sensor perception or generative AI (preferred)
Benefits
- 3-6 month full-time internship opportunity
- Work on autonomous trucking innovation at intersection of startups and expertise
- Mentorship from senior AI researchers
- Access to large-scale compute clusters and real truck datasets
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
Bot Auto
Bot Auto builds autonomous truck technology, developing the mechanical systems, deep learning models, and operational software that power self-driving commercial freight vehicles. The company is hiring mechanical engineers, machine learning engineers, software engineers, and interns to work across hardware design, perception and control systems, ML infrastructure, and fleet management platforms.
View all jobs at Bot AutoLikely interview questions
- Walk us through a deep learning project you've built from scratch—what was your architecture choice, and how did you validate it performed better than baselines?
- Describe your experience with Transformers. Have you applied them to a specific domain like vision or robotics, and what challenges did you encounter?