Autopilot - Deep Learning Research Engineer/Scientist Internship (Fall 2020)
Internship Program at Tesla
The University Recruiting Team is driven by the passion to recognize emerging talent. Our year around program places the best students in positions that they will grow both technically and personally through their experience working closely with their Manager, Mentor, and team. We are dedicated to providing an experience that allows for the intern to experience life at Tesla by given them projects that are critical to their team’s success. Instead of going on coffee runs and making copies, you’ll be seated at the table making critical decisions that will influence not only your team, but the overall achievement of Tesla’s mission.
Palo Alto, CA
About the Team
As a member of the Autopilot Vision team you will research, design, implement, optimize and deploy neural network models that advance the state of the art in autonomous driving. A strong candidate will ideally possess at least one strong expertise in the following areas, and at least a basic familiarity in others.
What to Expect
Train machine learning and deep learning models on a computing cluster to perform visual recognition tasks, such as segmentation, detection, self-supervised depth estimation and end-to-end control
Enhance the performance of neural networks with multi-task learning, large-scale distributed training, bayesian deep learning and uncertainty estimation, architecture search, multi-sensor fusion, etc.
- Minimum of MS or PHD education requirement
- Strong Python programming, software development best practices, debugging/profiling
- Extensive experience with at least one main stream deep learning framework such as PyTorch or TensorFlow
- Experience with some tensor processing library such as numpy, PyTorch tensors, etc.
- Background with neural network architecture patterns for computer vision (classification/segmentation/detection), natural language processing or speech recognition (CNNs, LSTMs, Mask-RCNN, etc.)
- Familiarity with data science toolkit such as jupyter lab/notebooks, pandas, bash scripting, Linux environment
- Solid understanding of algorithms, linear algebra, machine learning, computer systems/architecture, neural network under the hood details