We’re Blue River, a team of innovators driven to create intelligent machinery that solves monumental problems for our customers. We empower our customers – farmers, construction crews, and foresters - to implement safer and more sustainable solutions, driving increased profitability with less reliance on scarce labor. We believe that focusing on the small stuff – pixel-by-pixel and task-by-task - leads to big gains.

Blue River Technology aligns with John Deere’s vision to “innovate on behalf of humanity” by quickly identifying and solving high-value, high-uncertainty challenges in AI, machine learning, computer vision, and robotics. BRT acts as a research and development flywheel, building not only new products but also new platforms that reliably create value for both Deere and its customers. From fully autonomous machines to highly precise farming equipment, BRT and Deere are partnering to create technical breakthroughs in industries like agriculture and construction.

Our people are at the heart of what we do. Through cross-disciplinary collaboration, this mission-driven team is eager to define the new frontier of robotics. We are always asking hard questions, rapidly iterating, and getting our boots in the field to figure it out. We won’t give up until we’ve made a tangible and positive impact on the planet!

Blue River Technology is based in Santa Clara, CA. This position can be remote in the United States.

Summary

We are seeking a talented Radar Perception Engineer to join our team to develop and optimize the Radar Perception stack. You will play a critical role in enabling robust autonomy for our engineering machines, focusing on radar perception and data fusion with camera-based perception systems to advance our autonomous agricultural technology capabilities. You will work across the full stack, from hardware integration to algorithm development and validation.

Employment Type: Full-Time
Work Location: Remote in the United States
Visa sponsorship: Available for this position.

Job Responsibilities

A combination, not necessarily all-inclusive, of the following:

Software Development & Coding
Design and write robust, production-quality C++ code for real-time radar perception algorithms and robotics pipelines.
Apply modern software engineering best practices to maintain reusable, testable, and high-performance autonomy codebases.
Architecture & Integration
Architect and support scalable radar fusion pipelines for autonomous systems.
Lead the development and integration of radar data products within broader autonomy perception stacks.
Drive cross-functional collaboration to enhance sensor fusion performance and system reliability.
Algorithm Development
Develop and refine point cloud processing methods, including tracking and object classification and identification algorithms.
Implement odometric solutions and data fusion points for INS.
Validation & Metrics
Lead verification and validation efforts for radar perception systems through comprehensive field testing.
Drive system performance optimization and regression testing to ensure robust perception capabilities.
Collaborate with cross-functional teams to validate system safety and performance against rigorous field operational requirements.
Safety & Reliability
Develop strategies for safety-critical edge case detection, ensuring the perception system maintains robust performance in diverse agricultural environments.
Maintain high-quality technical documentation for perception architectures, test results, and deployment guidelines to ensure code maintainability across the team.
Proactively profile system bottlenecks (CPU/memory/latency) and implement optimizations to ensure perception pipelines meet real-time constraints on deployed robotic platforms.

Required Experience and Skills

BS/MS in Robotics, Computer Science, Engineering, or a related field.
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, with the ability to write production-level embedded code.