RL Engineer, End-to-End Autonomy

Blue River Technology•Santa Clara, CA

1d•$192,000 - $378,000•Hybrid

About The Position

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 an 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.

Requirements

Proven track record in developing RL models. Prefer experience deploying to production for robotics and boosting key metrics.
Expertise in developing simulation environments and tackling the sim-to-real transfer.
Expertise in designing and developing software for complex systems.
Comfortable working on new hardware systems and working on new RL/ML/software problems.
Strong Python coding skills and proficiency with deep learning frameworks like PyTorch.
Comfortable working across traditional team boundaries to deliver results.
Excellent brainstorming, creative thinking, mathematical analysis, and communication skills.
Track record of regularly anticipating technical issues and making architectural and design decisions to avoid them.
BS/MS/PhD in CS or related field, and 3+ years delivering high-performance RL products professionally.

Nice To Haves

Experience with robotics middleware such as ROS or other robotics-focused software packages.
Strong CUDA background or other GPU frameworks.
Experience in multidisciplinary environments. (We’ve got CS, CV/ML, EE, ME, etc.)
Have worked on embedded systems.
Experience with system architecture.

Responsibilities

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.
Collaborate with pretraining and robotics engineers to integrate RL seamlessly into the full E2E autonomy stack.
Drive the E2E vision and act as an ambassador for an E2E-first organization.
Technical leadership and mentorship: serve as an in-house RL expert, elevating the team through code reviews, algorithmic guidance, and fostering a culture of rigorous scientific experimentation.
Stay up to date with the latest RL research and integrate advancements into our stack.