- Robot type
- Autonomous Vehicle · Robot AI and Software
- Location
- SunnyvaleCaliforniaUSA
- Job type
- Software
- Posted
- Sep 25, 2026
- Salary
- $190,000–$265,000 a year
Software Engineer - Onboard Maps and State Estimation
Job description
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.;
Job responsibilities
- Own the onboard live-map estimator end to end: fuse HD map priors with live perception into a single, real-time estimate of the world the rest of the stack can trust
- Build our onboard map change detection: classify situations like boundary shifts, stop-line moves, and road closures from the divergence between map and perception, and define when the planner should stop trusting the…
- Define the map trust contracts the planner builds against: confidence semantics, degraded-mode behavior, and the operating points that hold across vehicle programs
- Build the evaluation backbone, ground-truth and metrics pipelines, operating points, and regression testing that keep estimator and detector quality from silently drifting
- Ship estimation code in modern C++ that meets real-time budgets on embedded compute
- Extend the loop offboard over time: aggregate divergence detections across fleet drive logs into map freshness and correction pipelines
Job requirements
- Bachelor's degree in Computer Science or equivalent
- 5+ years building production software, including at least one shipped system where they owned the estimation core
- State estimation fundamentals proven in production: factor graphs and/or filtering, data association, uncertainty representation and propagation, and 3D geometry and transforms
- Experience fusing heterogeneous sources such as prior map data, perception output, and raw sensors, into a single world estimate, with rigorous error characterization
- Strong modern C++ and the ability to make estimation code meet real-time budgets on embedded compute
- Rigorous evaluation practice: has built ground-truth or metrics pipelines and used them to hold a shipped operating point and prevent regressions
- Judgment about uncertainty: understands the difference between an error that degrades gracefully and one that is catastrophic, and designs the system accordingly
- Experience defining a system and its interfaces and negotiating those contracts with consumer teams
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