Radar Perception Engineer

Blue River Technology

2h•$131,000 - $228,000•Remote

About The Position

Blue River Technology is seeking a Radar Perception Engineer to develop and optimize its radar perception stack for autonomous engineering machines and agricultural technology. The role focuses on radar perception and data fusion with camera-based perception systems, spanning hardware integration, algorithm development, and validation. The position is remote within the United States and is full-time. Visa sponsorship is available. The engineer will develop production-quality, real-time radar perception software; architect and integrate radar fusion pipelines into autonomy perception stacks; and improve point-cloud processing, tracking, object classification, identification, and INS data fusion. The role also leads field testing, verification and validation, performance optimization, regression testing, and safety-focused edge-case analysis. It includes profiling CPU, memory, and latency bottlenecks on deployed robotic platforms and maintaining technical documentation. Blue River Technology is based in Santa Clara, California, and works with John Deere to develop AI, machine learning, computer vision, and robotics technologies for industries including agriculture and construction. The advertised annual base-pay range is $131,000–$228,000, with eligibility for an annual performance bonus and a competitive benefits package. The company describes its workplace as inclusive and emphasizes diversity, mentorship, career development, and learning and development.

Requirements

Bachelor’s or master’s degree in Robotics, Computer Science, Engineering, or a related field.
At least 3 years of professional experience in perception system development, with a strong focus on radar, robotics, and embedded systems.
Strong proficiency in C++ and Python, including the ability to write production-level embedded code.
Experience optimizing perception algorithms for real-time performance on resource-constrained embedded hardware, such as NVIDIA Jetson, ARM-based platforms, or similar.
Proficiency with ROS or ROS2 and familiarity with Linux/POSIX development environments.
Familiarity with point-cloud processing, sensor fusion, and coordinate-system transformations, including object identification, classification, and tracking.
Ability to work collaboratively across hardware, perception, and autonomy teams.

Nice To Haves

Experience with autonomous agricultural machinery, construction equipment, autonomous vehicles, or off-highway robotics platforms.
Experience with radar-camera sensor fusion, including sensor calibration, time synchronization, and multi-sensor data integration.
Familiarity with safety-critical perception systems, edge-case analysis, and robustness testing in challenging environmental conditions.

Responsibilities

Design and write robust, production-quality C++ code for real-time radar perception algorithms and robotics pipelines.
Apply modern software engineering practices to maintain reusable, testable, high-performance autonomy codebases.
Architect and support scalable radar fusion pipelines for autonomous systems.
Lead the development and integration of radar data products within broader autonomy perception stacks.
Collaborate across functions to improve sensor-fusion performance and system reliability.
Develop and refine point-cloud processing methods, including tracking and object classification and identification algorithms.
Implement odometric solutions and data-fusion points for INS.
Lead verification and validation of radar perception systems through field testing.
Optimize system performance and conduct regression testing.
Collaborate with cross-functional teams to validate system safety and performance against field operational requirements.
Develop strategies to detect safety-critical edge cases and maintain robust perception in diverse agricultural environments.
Maintain technical documentation for perception architectures, test results, and deployment guidelines.
Profile CPU, memory, and latency bottlenecks and optimize perception pipelines to meet real-time constraints on deployed robotic platforms.