Machine Learning Engineer, Blue River Technology

• Proven track record in developing ML models. Prefer experience deploying to production for end-to-end robotics and boosting key metrics
• Expertise in designing and developing software for complex systems
• Comfortable working on new hardware systems and working on new 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
• If you’re excited about this role but your experience doesn’t align completely with the job description, we encourage you to apply anyway

Desirable

• As an ML 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
• Design and implement end-to-end policies for complex navigation and manipulation tasks with long horizons
• Enhance policy robustness to challenges, including environmental variability, machine wear, and deployment across machine forms
• Drive the full data + modeling life cycle, from data collection requirements, experimental design, evaluation frameworks, model training, and deployment. Iteratively drive performance improvements
• Develop pretraining strategies leveraging multimodal data at scale. Explore approaches to leveraging John Deere’s vast store of machine logs, and bring an E2E lens to inform future logging vision
• Edge deployment: help deliver performance with constrained compute
• Design and implement systems for active learning on the edge
• Collaborate with infrastructure engineers on scalable data pipelines, including ingest, curation, and processing training artifacts
• Collaborate with infrastructure engineers on scalable distributed training pipelines
• Collaborate with reinforcement learning, robotics, and hardware engineers to integrate ML seamlessly into the full E2E autonomy stack
• Work with product management to learn from customers, including how they want to interface with the product. Translate this to ML architecture and implement
• Contribute to the design, development, and validation of perception systems for safety
• Help shape the theory of operations and build the safety testing framework
• Drive the E2E vision and act as an ambassador for an E2E-first organization
• Technical leadership and mentorship: guide the team through complex algorithmic tradeoffs, elevate our approach to empirical testing, and mentor the next wave of E2E robotics leaders
• Stay up to date with the latest research and integrate advancements into our stack