SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Applied Intuition is a $15B-valued AI company (founded 2017) powering autonomous systems across automotive, defense, trucking, construction, mining, and agriculture. The Localization, Calibration, and Mapping team delivers high-integrity solutions for real-time navigation in safety-critical environments.
You will own the live map on Applied's onboard stack—a real-time fused estimate of the world built from HD map priors and live perception. This is early-stage, greenfield work with no incumbent design; you'll set the architecture and define interfaces rather than optimize within constraints.
Key responsibilities:
- Own the onboard live-map estimator end-to-end: fuse HD map priors with live perception into a single, real-time world estimate the rest of the stack can trust
- Build onboard map change detection: classify boundary shifts, stop-line moves, road closures from map-perception divergence; define when the planner should stop trusting the map
- Define map trust contracts for the planner: confidence semantics, degraded-mode behavior, operating points across vehicle programs
- Build evaluation backbone: ground-truth and metrics pipelines, operating points, regression testing to prevent silent quality drift
- Ship estimation code in modern C++ meeting 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
You will interact closely with users to collect feedback and take ownership of technical and product decisions. The company expects full-time employees to work in-office 5 days per week from the Sunnyvale headquarters, 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, 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: built ground-truth or metrics pipelines, used them to hold a shipped operating point and prevent regressions
- Judgment about uncertainty: understand the difference between graceful degradation and catastrophic failure; design systems accordingly
- Experience defining a system and its interfaces, 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 failure modes
- GTSAM or comparable factor-graph frameworks; Lie group methods
- Degraded-mode or integrity-monitor design for safety-relevant functions
- MS/PhD with focus on state estimation or SLAM
