Autonomy and Robotics Software Engineer

Job description

Job responsibilities

• Design, implement, and ship production autonomy software in modern C++ on Linux, with Python for tooling, analysis, and ML workflows.
• Build and integrate capabilities across perception, localization, sensor fusion, navigation, planning, and embedded inference.
• Take systems from prototype through simulation, software-in-the-loop, hardware-in-the-loop, flight test, and deployment.
• Analyze logs, simulation results, and test data to debug failures, improve robustness, and drive rapid iteration.
• Develop evaluation pipelines, metrics, and tooling for accuracy, latency, drift, handoff stability, and mission-level performance.
• Work closely with hardware, sensing, flight test, and ML teams to integrate algorithms onto real platforms.
• Optimize software and models for real-time deployment on embedded compute under tight SWaP and latency constraints.
• Design systems for degraded operation, fault detection, graceful degradation, and uncertainty-aware decision-making in contested environments.

Job requirements

• Bachelor’s, Master’s, or PhD in Computer Science, Robotics, Aerospace, Electrical Engineering, Machine Learning, or a related field, or 3+ years of equivalent practical experience as a software engineer.
• Strong software engineering skills in modern C++ on Linux and Python for tooling, analysis, or ML.
• Proven ability to take systems from research or prototype into reliable deployment on hardware.
• Deep technical strength in at least one core domain such as perception, localization and state estimation, navigation, planning, machine learning, or embedded autonomy.
• Experience building and debugging real-world robotics, autonomy, or embedded software systems.
• Experience with state estimation, SLAM, VIO, GNSS/INS fusion, relocalization, or multi-sensor fusion.
• Experience with perception systems for detection, segmentation, tracking, pose estimation, or classification in challenging real-world conditions.
• Experience training, fine-tuning, or deploying ML models for autonomy applications.