AI Research Scientist, Autonomous Robotics
3 weeks ago
Tesla, Inc. is revolutionizing the manufacturing and logistics industry by developing humanoid bi-pedal robots that can automate repetitive and boring tasks.
As an AI Research Scientist in our autonomous robotics team, you will have the opportunity to work on state-of-the-art neural network algorithms for deep learning, which will be deployed to real-world production applications.
We are seeking a highly skilled professional who can develop and own the deep learning stack from inception to deployment.
Your Role:
You will train machine learning and deep learning models on a computing cluster for visual recognition & perception tasks, such as segmentation and detection, and world representation applications.
Developing 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, LLMs, etc.
Optimizing deep neural networks and the associated preprocessing/postprocessing code to run efficiently on an embedded device.
What We Offer:
A competitive annual salary of $185,000 - $380,000, plus stock awards benefits.
The total compensation package for this position may also include other elements dependent on the position offered.
Key Responsibilities:
- Developing and deploying real-world neural networks
- Training machine learning and deep learning models on a computing cluster
- Developing state-of-the-art algorithms in deep learning
- Optimizing deep neural networks and the associated preprocessing/postprocessing code
- Working on a team to develop and own the deep learning stack
Requirements:
Experience writing both production-level Python (including Numpy and Pytorch) and modern C++.
Experience in deep imitation learning or reinforcement learning in realistic applications.
Exposure to robotics learning through tactile and/or vision-based sensors.
A proven track record of training and deploying real-world neural networks.
Familiarity with 3D computer vision and/or graphics pipelines.
Experience with distributed deep learning systems.
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