About the position
Tesla is a leader in innovative technology, pioneering advancements in autonomous vehicles and humanoid robotics. Our cutting-edge AI platform powers some of the most advanced systems globally, including Full Self Driving for vehicles and humanoid robots. As a Machine Learning Engineer for Integration within our Autonomy teams, you will develop AI-based solutions for accessory controls of FSD, including the efficient control of cleaning, heating, and fault recovery strategies. Additionally, you will work with vehicle hardware and firmware teams to design vision-based solutions for enhancing vehicle systems that improve customer safety and experience. The team contributes to the development and deployment of new features on both vehicles and robotics platforms. Join us in shaping the future of mobility and robotics technology.
Responsibilities
- Leverage millions of miles of driving data and interventions to build robust and scalable end-to-end learning-based systems
- Combine multiple data sources, including video, audio, thermal, mechanical, weather, and other sensors to develop and train end-to-end models for control of all vehicle accessories required for autonomous operation
- Define data collection and annotation strategies for training and evaluation
- Integrate directly with vehicle firmware and ship production quality, safety-critical software to the entirety of Tesla's vehicle fleet
- Iterate on new features through simulation, validation, and fleet data analysis
Requirements
- Proficiency in writing production quality code in Python, and experience with any major deep learning framework
- Proficiency in modern C++ to integrate with vehicle firmware and take projects from ideas to shipped products
- An 'under the hood' knowledge of deep learning modern architectures, optimization, model alignment, etc
- Ability to think 'out-of-the-box' to push performance beyond apparent limitations
- Excellent problem-solving, critical thinking, and communication skills
