Applied Intuition
Sensor Sim - ML Engineer
About this role
Applied Intuition is seeking an ML Engineer to integrate cutting-edge machine learning techniques into production sensor simulation for autonomous vehicles. You'll collaborate with research and engineering teams to develop generative models and advanced approaches for simulating LiDAR, Radar, and Camera sensors that drive real customer impact.
What you'll do
- Research, develop, and deploy generative and machine learning techniques into the sensor simulator
- Collaborate with rendering and physics-based modeling teams to create hybrid approaches
- Optimize and evaluate large generative models for production use
- Design and implement agentic systems for sensor simulation
- Focus on projects with clear paths to production value and measurable customer impact
- Develop tooling to enhance productivity for autonomous systems teams
What they're looking for
- Machine learning foundations and advanced techniques
- Generative model evaluation and optimization
- 3D geometry, optical flow, or video generation
- Agentic system design
- Software architecture and systems design
- Python or systems programming languages
- Generative World Models (Cosmos, UniSim, etc.)
- Robotics simulation platforms (Isaac Sim, MuJoCo, Omniverse)
Benefits
- Competitive base salary plus equity compensation
- Comprehensive health, dental, vision, life and disability insurance
- 401k retirement benefits with employer match
- Learning and wellness stipends
- Paid time off
- Flexible work arrangements with primarily in-office expectations
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Applied Intuition
Applied Intuition builds autonomous vehicle and defense systems software, including motion planning algorithms, simulation infrastructure, and autonomy integration platforms for aerial and ground platforms. The company is hiring for security engineers, robotics/autonomy software engineers, hardware-in-the-loop specialists, and IT operations professionals to support its growing physical AI operations.
- Website
- appliedintuition.com
Likely interview questions
- Can you walk us through a project where you deployed a generative model or large language model to production, and how you approached evaluation and optimization?
- Describe your experience with sensor simulation or physics-based modeling—how have you integrated ML techniques with rendering or physics systems?