Applied Intuition
Software Engineer - Perception (Fallback Stack)
About this role
Applied Intuition seeks a Software Engineer to develop perception capabilities for autonomous vehicles that detect uncertainty, degradation, and failures to trigger safe fallback behavior. You'll design simulation systems and deploy production solutions that enhance vehicle safety and operational domain expansion.
What you'll do
- Design and implement simulation capabilities for real-world perception system development
- Collaborate with sensor simulation team to build multi-fidelity simulation models
- Work with infrastructure team to provide enterprise software for engineering and testing
- Design perception and uncertainty signals that trigger fallback behaviors
- Deploy production systems impacting vehicle safety and ODD expansion
What they're looking for
- Machine learning uncertainty estimation and confidence calibration
- Out-of-distribution (OOD) detection
- C++ and/or Python
- Production ML/DL perception algorithms for autonomous vehicles
- Software frameworks and tools (middleware, benchmarking, datasets, algorithmic libraries)
- Safety-critical systems development
- Non-linear optimization or computational geometry (nice to have)
- ISO/SOTIF safety standards knowledge (nice to have)
Benefits
- Base salary, equity (options/RSUs), and comprehensive health, dental, vision insurance
- Life and disability insurance coverage
- 401(k) retirement with employer match
- Learning and wellness stipends
- Paid time off
- Flexible work arrangements with occasional remote work option
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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 specific production ML/DL perception algorithm you've deployed in an autonomous vehicle system, and how you validated its safety and reliability?
- Describe your experience with uncertainty estimation or out-of-distribution (OOD) detection methods. How have you used these techniques to improve system robustness?