Software Engineer - Map Pipeline & Automation

Overview As a Software Engineer specializing in Map Pipeline & Automation, you will develop scalable data pipelines and automated workflows that transform raw sensor data and external map sources into reliable, high-quality maps for autonomous driving. Working closely with mapping, perception, and autonomous driving teams, you will build tools for map processing, validation, versioning, and deployment. This role offers the opportunity to work with large-scale geospatial data, distributed computing infrastructure, and modern software engineering technologies to support the development and continuous improvement of our autonomous driving systems. Responsibilities Develop and maintain automated map production pipelines, covering data ingestion, alignment, map conflation, format conversion, validation, packaging, and release. Build automated map validation tools to detect geometric inconsistencies, topology errors, connectivity issues, and missing or incorrect map attributes. Develop scalable map storage and retrieval services, including map indexing, tile serving, lane-level queries, and APIs for autonomous driving applications. Implement map format conversion and export tools, supporting industry-standard formats such as OpenDRIVE and Lanelet2 for simulation, planning, and evaluation. Build robust data processing workflows for integrating vehicle sensor logs, HD maps, and third-party geospatial datasets. Improve map pipeline reliability and efficiency through automated testing, monitoring, CI/CD, and infrastructure automation. Develop visualization and debugging tools to support map inspection, quality assessment, and investigation of mapping errors. Collaborate with mapping, perception, simulation, and autonomous driving teams to integrate map data and services into training, testing, and deployment workflows. Explore ML-assisted map automation techniques, including lane extraction, traffic sign detection, map change detection, and automated map updates. Contribute clean, efficient, and maintainable code to shared mapping infrastructure, processing pipelines, and development tools. Requirements 3+ years of practical experience in software engineering, data engineering, robotics, geospatial computing, or related fields. Bachelor's or Master's degree in Computer Science, Robotics, Software Engineering, or a related discipline, or equivalent practical experience. Strong programming skills in Python and C++, with experience developing reliable data processing pipelines and backend services. Solid understanding of software engineering fundamentals, including data structures, algorithms, system design, and automated testing. Experience with Linux-based development environments, containerization technologies such as Docker, and workflow orchestration tools. Familiarity with cloud computing, distributed data processing, or scalable storage infrastructure. Experience working with relational databases, geospatial databases, or large-scale structured datasets. Familiarity with developing and maintaining backend services and APIs using REST or gRPC. Good analytical and problem-solving skills, with the ability to debug complex data workflows and improve system performance. Ability to collaborate effectively with researchers and engineers in a fast-paced R&D environment. Preferred Qualifications Experience with geospatial data processing, coordinate reference systems, or HD map representations. Familiarity with autonomous driving map formats and tools such as Lanelet2, OpenDRIVE, or similar frameworks. Knowledge of LiDAR point cloud processing, SLAM, pose estimation, or pose-graph optimization. Experience developing automated map validation, map conflation, or map change detection pipelines. Familiarity with GIS tools and geospatial libraries such as PostGIS, GDAL, or similar technologies. Experience with distributed computing, cloud infrastructure, and workflow orchestration frameworks. Knowledge of machine learning approaches for lane detection, road topology estimation, or automated map generation. Familiarity with frontend technologies such as React or TypeScript for building internal visualization and debugging tools. Experience with autonomous driving, robotics, navigation, or simulation systems. Understanding of map versioning, continuous map updates, and data quality management for autonomous driving. 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