RL Engineer

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. With our partners at John Deere, we have the ability to bring innovative computer vision, machine learning, robotics, and product management solutions to scale production, maximizing their potential impact.

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

Summary

Our mission is to bring the power of E2E machine learning and robotics to John Deere, revolutionizing how robotic systems are built. We aim to open up previously inaccessible opportunities, delivering autonomy products that customers love.

As a Principal/Sr Principal RL Engineer, you will help grow and shape our E2E stack. This involves shaping the design of the E2E training and inference pipeline, both on- and off-vehicle and on- and off-prem. You will also help with new models and features. Our E2E team is lean and moving quickly. You should be comfortable and excited about working with ambiguity, helping define what will move the program forward, working across traditional boundaries, and learning new things.

• Employment Type: Full-Time
• Work Location: Remote
• Visa sponsorship is available for this position. 

Job Responsibilities

The main job responsibilities include: 

• Design and implement state-of-the-art online and offline reinforcement learning algorithms for complex tasks with long horizons. 
• Formulate reward models and exploration strategies that enable task performance and adherence to strict safety requirements. Enable flexible customer operation across a range of tasks with natural language instructions.
• Enhance policy robustness to challenges such as sensor noise, machine wear, and extreme environmental variability.
• Design and conduct experiments; develop evaluation frameworks for simulation and real-world deployment.
• Collaborate closely with infrastructure engineers to design scalable E2E training systems, including large-scale simulation infrastructure.