Autonomy and Robotics Software Engineer

- 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.

- 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.

- Experience bringing up complex embedded systems across sensors, compute, vehicle interfaces, and autonomy software on embedded Linux or RTOS platforms.

- Experience with motion planning, mission autonomy, or decision-making under uncertainty.

- Strong engineering judgment across system-level tradeoffs including latency, accuracy, robustness, and operational reliability.

- High standards for validation through metrics, simulation, logs, and field testing.

- Experience with simulation, HITL, synthetic data, and sensor modeling.

- Experience in contested or degraded environments, including RF denial, GNSS degradation, low-light or night operations, or high-vibration platforms.

- Strong data and infrastructure practices, including dataset versioning, reproducible pipelines, CI-based validation, and evaluation tooling.