Senior Robotics Software Engineer, Perception

Parallel Systems•Los Angeles, CA

About The Position

Parallel Systems is pioneering autonomous battery-electric rail vehicles designed to transform freight transportation by shifting portions of the $900 billion U.S. trucking industry onto rail. Our innovative technology offers cleaner, safer, and more efficient logistics solutions. Join our dynamic team and help shape a smarter, greener future for global freight. Parallel Systems is seeking a Senior Robotics Software Engineer to develop the perception and sensor fusion algorithms that let our fully autonomous, battery-electric rail vehicles understand the world around them in real time. In this role, you'll design and implement geometry-based computer vision and robotics algorithms alongside ML/learned-based methods, fuse high-throughput data from lidar, camera, IMU, and GPS sensors, and validate that the resulting system runs reliably around the clock in the field. You'll work closely with ML, firmware, embedded platform, and systems engineers to take sensor selection, calibration, and real-time fusion from first principles through production deployment. If you like working close to the math, the sensors, and the real world all at once, we'd love to work with you.

Requirements

Bachelor's or higher degree in Robotics, Computer Science, Electrical Engineering, or a related technical discipline; equivalent experience considered.
4+ years of hands-on experience developing and deploying robotics or perception software in real-world systems.
Strong background designing and implementing geometry-based computer vision and robotics algorithms (multi-view geometry, filtering, optimization); ML-based experience is a bonus.
Proficiency in Rust and C/C++.
Experience with real-time systems handling high-throughput, multi-modal sensor data and fusion across lidar, camera, IMU, and GPS.

Nice To Haves

Experience implementing and testing real-time sensor fusion code, including validation methodology.
Experience with sensor calibration and/or a strong foundation in math and optimization algorithms (Kalman/particle filters, non-linear optimization, 3D geometry).
Ability to evaluate sensor selection and system-level tradeoffs, and to define test and validation plans that prove robust, round-the-clock production operations.
Excellent communication and collaboration skills, with experience working on interdisciplinary teams.
Experience with sensor fusion, localization/SLAM, and/or online calibration.
Experience with GStreamer for real-time pipeline development.
Publications in top-tier robotics conferences (e.g., ICRA, IROS, RSS, CoRL) or related journals.
Hardware experience building IP-rated, ruggedized robotic systems and designing real-world robotic platforms.
Experience with ROS2 or similar robotics middleware.
Background in autonomous vehicles, field robotics, or other safety-critical domains.

Responsibilities

Build and maintain real-time robotics software that ingests and fuses high-throughput, multi-modal sensor data from lidar, camera, IMU, GPS, and other sensors.
Implement, validate, and test real-time sensor fusion code across simulation and real-world deployments, catching regressions before they reach the field.
Perform sensor calibration (intrinsic, extrinsic, temporal) and apply a strong foundation in linear algebra, 3D geometry, and optimization (e.g., Kalman/particle filters, non-linear optimization) to state estimation problems.
Design and implement geometry-based computer vision and robotics algorithms (multi-view geometry, filtering, tracking) for real-time perception; experience with ML-based approaches is a bonus.
Evaluate sensor selection and system-level tradeoffs, and define test and validation plans that prove the system can run reliably 24/7 in production.
Contribute to sensor system hardware design, including IP-rated, ruggedized enclosures and mounts built to hold up in real-world field conditions.
Build and maintain sensor pipelines for real-time sensor and video data streaming and processing.
Collaborate with ML, firmware, embedded platform, and systems engineering teams to integrate perception algorithms into the full autonomy stack.