Asks for

The ad says“State estimation fundamentals proven in production: factor graphs and/or filtering, data association, uncertainty representation and propagation, and 3D geometry and transforms”
The ad says“State estimation fundamentals proven in production: factor graphs and/or filtering, data association, uncertainty representation and propagation, and 3D geometry and transforms”
The ad says“Ship estimation code in modern C++ that meets real-time budgets on embedded compute”
The ad says“State estimation fundamentals proven in production: factor graphs and/or filtering, data association, uncertainty representation and propagation, and 3D geometry and transforms”
The ad says“State estimation fundamentals proven in production: factor graphs and/or filtering, data association, uncertainty representation and propagation, and 3D geometry and transforms”
The ad says“Experience fusing heterogeneous sources such as prior map data, perception output, and raw sensors, into a single world estimate, with rigorous error characterization”
The ad says“Rigorous evaluation practice: has built ground-truth or metrics pipelines and used them to hold a shipped operating point and prevent regressions”
The ad says“GTSAM or comparable factor-graph frameworks; Lie group methods”
The ad says“Degraded-mode or integrity-monitor design for a safety-relevant function”
The ad says“HD map semantics: lane graphs, topology, map formats and their failure modes”
The ad says“GTSAM or comparable factor-graph frameworks; Lie group methods”
The ad says“Map change detection or live/lifelong mapping experience”
The ad says“Map change detection or live/lifelong mapping experience”
The ad says“Ship estimation code in modern C++ that meets real-time budgets on embedded compute”
The ad says“MS/PhD with a focus on state estimation or SLAM”
The ad says“State estimation fundamentals proven in production: factor graphs and/or filtering, data association, uncertainty representation and propagation, and 3D geometry and transforms”
The ad says“HD map semantics: lane graphs, topology, map formats and their failure modes”
The ad says“State estimation fundamentals proven in production: factor graphs and/or filtering, data association, uncertainty representation and propagation, and 3D geometry and transforms”

Our Localization, Calibration, and Mapping team delivers high-integrity solutions that let autonomous systems navigate with precision in real-time, safety-critical environments — across consumer automotive, trucking, mining, and construction
As a new team focused on platformization, we offer a rare opportunity to own core work leveraged across the entire organization
HD maps go stale the moment they’re built — lanes get repainted, stop lines move, roads close
You’ll own the live map on Applied’s onboard stack: a real-time fused estimate of the world built from map priors and live perception, from the estimation core to the change-detection and trust semantics the planner consumes, across every vehicle vertical we serve
This layer is early and has no incumbent design
You’ll set the architecture and define the interfaces rather than tune inside someone else’s system
We encourage engineers to take ownership of technical and product decisions and to interact closely with users to collect feedback
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 map
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

Preferred experience

State estimation fundamentals proven in production: factor graphs and/or filtering, data association, uncertainty representation and propagation, and 3D geometry and transforms
Experience defining a system and its interfaces and negotiating those contracts with consumer teams
Strong modern C++ and the ability to make estimation code meet real-time budgets on embedded compute
Bachelor’s degree in Computer Science or equivalent
5+ years of experience building production software, including at least one shipped system where they owned the estimation core
Experience fusing heterogeneous sources such as prior map data, perception output, and raw sensors, into a single world estimate, with rigorous error characterization
Judgment about uncertainty: understands the difference between an error that degrades gracefully and one that is catastrophic, and designs the system accordingly
Rigorous evaluation practice: has built ground-truth or metrics pipelines and used them to hold a shipped operating point and prevent regressions
Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles
MS/PhD with a focus on state estimation or SLAM
Degraded-mode or integrity-monitor design for a safety-relevant function
GTSAM or comparable factor-graph frameworks; Lie group methods
HD map semantics: lane graphs, topology, map formats and their failure modes
Map change detection or live/lifelong mapping experience