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Applied Intuition is a $15B-valued company (founded 2017) powering physical AI infrastructure for autonomous systems across automotive, defense, trucking, construction, mining, and agriculture. The company serves 18 of the top 20 global automakers and the US military.
You will own the onboard live-map estimator end-to-end for Applied's autonomous vehicle stack. This is a rare opportunity to architect a core system from the ground up with no incumbent design—you'll set the architecture and define interfaces rather than optimize within existing constraints.
Key responsibilities:
- Own the onboard live-map estimator: fuse HD map priors with live perception into a single, real-time estimate of the world that the rest of the stack can trust
- Build onboard map change detection: classify situations like boundary shifts, stop-line moves, and road closures from divergence between map and perception; define when the planner should stop trusting the map
- Define map trust contracts the planner builds against: confidence semantics, degraded-mode behavior, and operating points that hold across vehicle programs
- Build evaluation backbone, ground-truth and metrics pipelines, operating points, and regression testing to prevent quality drift
- 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
Applied Intuition is an in-office company with expectation that full-time employees work from their office 5 days a week, with flexibility for occasional remote work and schedule adjustments.
REQUIREMENTS:
- 5+ years building production software, including at least one shipped system where you 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 (prior map data, perception output, raw sensors) into a single world estimate, with rigorous error characterization
- Strong modern C++ and 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
NICE TO HAVE:
- Map change detection or live/lifelong mapping experience
- HD map semantics: lane graphs, topology, map formats and their failure modes
- GTSAM or comparable factor-graph frameworks; Lie group methods
- Degraded-mode or integrity-monitor design for a safety-relevant function
- MS/PhD with a focus on state estimation or SLAM
