Tesla is on a path to build humanoid bi-pedal robots at scale to automate repetitive and boring tasks for manufacturing/logistics. Core to the Tesla Bot, the deep learning stack presents a unique opportunity to work on state-of-the-art neural network algorithms for deep learning culminating in their deployment to real world production applications. Our deep learning research scientists and engineers develop and own this stack from inception to deployment.
- Train machine learning and deep learning models on a computing cluster to perform visual recognition tasks, such as segmentation and detection
- Develop state-of-the-art algorithms in one or all of the following areas: deep learning (convolutional neural networks), object detection/classification, tracking, multi-task learning, large-scale distributed training, multi-sensor fusion, dense depth estimation, etc.
- Optimize deep neural networks and the associated preprocessing/postprocessing code to run efficiently on an embedded device
The team operates in a production setting. An ideal candidate has strong software engineering practices and is very comfortable with Python programming, debugging/profiling, and version control.
- We train neural networks on a cluster in large-scale distributed settings. An ideal candidate is very comfortable in cluster environments and understands the related computer systems concepts (CPU/GPU interactions/transfers, latency/throughput bottlenecks during training of neural networks, CUDA, pipelining/multiprocessing, etc).
- We are at the cutting edge of deep learning applications. The ideal candidate has a strong understanding of the under the hood fundamentals of deep learning (layer details, backpropagation, etc). Additional requirements include the ability to read and implement related academic literature and experience in applying state of the art deep learning models to computer vision (e.g. segmentation, detection) or a closely related area (speech, NLP).
- Experience with PyTorch, or at least another major deep learning framework such as TensorFlow
- Some experience with data science tools including Python scripting, numpy, scipy, matplotlib, scikit-learn, jupyter notebooks, bash scripting, Linux environment.