We are seeking a Staff Geospatial Data Engineer to lead the architectural evolution of geospatial data prep and routing infrastructure for our Large Driving Model (LDM). In this role, you will bridge the gap between complex digital map representations, routing engines, and data pipelines for large-scale AI training. Your primary focus will be designing and optimizing high-throughput route processing systems, map-matching algorithms, and interfaces that transform raw spatial topology into ML-ready representations for onboard and offboard pipelines. This is a high-impact position sitting at the intersection of spatial data engineering, graph algorithms, and production-grade AI infrastructure. You will be directly responsible for the C++ and Python development required to process massive routing graphs efficiently, ensuring seamless integration between map data and model inputs. If you are a seasoned engineer with a deep algorithmic foundation in routing, map interactions, and a data-first mindset, we want to hear from you.

Route Processing Pipeline Architecture:

Design, build, and own high-performance software related to route processing part of the data pipeline which serves as the foundational data preparation layer for our Large Driving Model (LDM).

Routing Engine Integration:

Lead the integration, customization, and optimization of spatial frameworks and routing engines to support complex driving scenarios and route parsing.

Data Prep & Map Quality:

Establish techniques to clean, validate, align, and enrich map features, minimizing data discrepancies between offboard training data prep and onboard deployment.

Cross-Functional Collaboration:

Partner closely with ML Infrastructure, Motion Planning, Localization, Mapping, and Simulation teams to define clean data interfaces and translate topological map data into effective model features.

Technical Leadership:

Act as a key technical authority on route data processing, spatial graphs, and routing architecture. Mentor junior engineers, establish best practices for spatial data handling, and define the long-term roadmap.

Foster Engineering Excellence:

Champion a culture of production-grade C++ and Python code, ensuring the route processing codebase is modular, scalable, thoroughly tested, and performant. B.S., M.S., or Ph.D. in Computer Science, Robotics, Electrical Engineering, or a related technical field. 8+ years of industry experience developing high-performance route processing algorithms, GIS/spatial data pipelines, or routing systems for autonomous platforms or large-scale mapping products. Expert C++ &

Python:

Mastery of production-grade C++ (data structures, memory management, multi-threading) alongside Python for data engineering, automation, and rapid prototyping.

Routing & Map Algorithms:

Deep technical foundation in spatial graph algorithms (A

•, Dijkstra, contraction hierarchies), map-matching, spatial indexing, and topological data manipulation.

Routing Engines & Map Formats:

Hands-on experience with OpenStreetMap (OSM) data structures and modern open-source routing engines (e.g., Valhalla, OSRM) is a strong plus.

Localization & Autonomous Context:

Familiarity with localization concepts, spatial coordinate transformations, and HD/SD map abstractions within autonomous driving stacks.

Leadership & Communication:

Proven track record leading complex software projects, building technical consensus, and mentoring engineering peers.

Hands-on Approach:

Proactively identifies data bottlenecks in the LDM pipeline and "rolls up sleeves" to design and implement robust algorithmic solutions.