Job Description
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Company Overview
A Silicon Valley technology company focused on building digital infrastructure for physical AI and autonomous systems. The company develops tools, infrastructure, operating systems, and autonomy solutions for industries including automotive, defense, trucking, construction, mining, and agriculture. Its technology is used by major global automakers and government organizations to support the development and deployment of intelligent physical systems.
Responsibilities
- Drive the research, development, and deployment of generative and other machine learning techniques for production-grade sensor simulation.
- Work with research teams to apply state-of-the-art machine learning approaches to modeling simulated worlds and sensors, including LiDAR, radar, and cameras.
- Collaborate closely with rendering and physics-based modeling teams to develop novel approaches that combine machine learning with traditional simulation techniques.
- Develop projects with a clear path toward production value and meaningful customer impact.
- Apply machine learning expertise to real-world autonomous systems and sensor simulation challenges.
- Develop tooling and solutions that improve the productivity of teams working on real-world applications of autonomous systems.
- Evaluate and optimize large generative models for sensor simulation applications.
- Design and implement agentic systems and machine learning workflows for production use.
Qualifications
- Bachelor’s degree in Computer Science, Software Engineering, or an equivalent field.
- 5+ years of experience building software components or subsystems that address real-world machine learning challenges.
- Strong understanding of machine learning fundamentals and the ability to apply different techniques to new and complex problems.
- Hands-on experience evaluating and optimizing large generative models.
- Hands-on experience with agentic system design.
- Strong interest in translating machine learning and domain expertise into practical tools for autonomous systems applications.
- Understanding of 3D geometry, optical flow, or video generation.
- Experience with Generative World Models, such as Cosmos, UniSim, or Sora-style architectures, is a plus.
- Experience with robotics simulation platforms such as Isaac Sim, MuJoCo, Omniverse, or USD is a plus.
- Experience applying synthetic data to machine learning tasks is a plus.
- Hands-on experience characterizing models for LiDAR, radar, and camera systems is a plus.
Work Environment
- This is an in-office position based in Sunnyvale, California.
- Full-time employees are generally expected to work from the office five days per week.
- Occasional flexibility for remote work or adjusted schedules may be available depending on business and personal circumstances.
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