Software Engineer - Perception; Fallback Stack

About Applied Intuition

Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy.

Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence.

Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.;
San Diego;
Ft. Walton Beach, Florida;
Ann Arbor, Michigan;
London;
Stuttgart;
Munich;
Stockholm;
Bangalore;
Seoul; and Tokyo. Learn more at

We are looking for a software engineer with expertise in perception for autonomous vehicles or mobile robots. Your contributions will focus on building out key capabilities of our core simulation engine. As a Perception Engineer on the Fallback Stack team, you will build perception capabilities that help autonomous vehicles recognize uncertainty, degradation, and failure—and respond safely. Your work ensures the system knows its limits and can trigger safe fallback behavior or human assistance when needed.

In addition to your engineering contributions, by working in our dynamic and customer‑focused team culture, you will contribute to and learn from best practices in the nascent autonomy industry. We move fast and focus on excellence, for our products and for our business. If you are hands‑on and looking for a place to have a multiplying effect on making autonomous systems a reality, Applied is the place for you!

• Design and implement simulation capabilities and workflows to enable cutting‑edge real‑world perception system development
• Work closely with our sensor simulation team to ensure we build the best range of multi‑fidelity simulation models
• Collaborate with our infrastructure team to provide the best enterprise software for engineering, testing, and product teams at our customers
• Design perception and uncertainty signals that gate fallback behaviors
• Deploy production systems that directly impact vehicle safety and ODD expansion

• Experience using ML for uncertainty estimation, confidence calibration, or OOD detection
• Is passionate about turning their domain expertise into tooling that boosts the productivity of teams working on various real‑world applications of autonomous systems
• 5+ years of experience in AV, robotics, or safety‑critical perception systems
• Proficiency in C++ and/or Python and production‑grade software practices
• Hands‑on experience with more than one domain‑relevant software framework or toolset, such as middleware, benchmarking suites, data sets, pipelines, or algorithmic libraries
• Worked with production‑level ML and DL perception algorithms for autonomous vehicles

• MSc or PhD in perception or closely related field
• Experience with degraded sensors, adverse weather, or real‑world edge cases
• Deep hands‑on expertise in relevant algorithms or methods, such as non‑linear optimization, computational geometry, numerical analysis, distributed systems
• Familiarity with fallback systems, MRM concepts, or safety cases (ISO/SOTIF)

Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off.

Note that benefits are subject to change and may vary by jurisdiction of employment. Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will also be influenced by experience, credentials…