Sr Platform Engineer, CVML Remote

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.

We are seeking a Senior CVML Platform Engineer to help design, build, and evolve the platforms that support computer vision and ML workloads s role focuses on enabling ML teams through well-designed infrastructure, tooling, and workflows, rather than developing models or conducting ML research.

The ideal candidate brings strong technical judgment, is comfortable navigating existing and evolving platforms, and can incrementally improve systems while maintaining reliability. We strongly prefer engineers with a Dev Ops or platform engineering background who have moved into ML‑adjacent systems and are motivated by building durable foundations that other teams rely on.

This role requires both hands‑on engineering and the ability to influence platform direction through collaboration and thoughtful design.

• Employment Type
  :
  Full-Time
• Work Location
  :
  Remote in the United States
• Visa sponsorship will be considered on a case‑by‑case basis.

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

• Platform Development & Ownership
  • Design, build, and evolve platform capabilities that support ML training, batch inference, and model deployment workflows at scale.
  • Own and improve core platform components (e.g., compute orchestration, data pipelines, inference systems) used by multiple teams across Blue River and John Deere.
  • Continuously enhance platform reliability, scalability, and performance, with a focus on real‑world ML workloads.
• ML Systems Enablement
  • Enable ML engineers to move faster by building intuitive, well‑documented platform tools and workflows across the model lifecycle (experimentation, deployment, and iteration).
  • Improve model inference performance and throughput while balancing trade‑offs among cost, latency, and reliability.
  • Support and scale distributed training and inference systems, including frameworks such as Ray and related tooling.
• Infrastructure & Compute Systems
  • Develop and optimize hybrid compute environments (cloud + on‑prem/GPU infrastructure) to support large‑scale ML workloads.
  • Build and maintain infrastructure leveraging Kubernetes, Slurm, and cloud platforms (AWS preferred).
  • Identify and resolve bottlenecks in compute, storage, and data movement pipelines.
• System Evolution & Technical Judgment
  • Evaluate existing platform systems and make thoughtful decisions on when to extend, refactor, or rebuild components.
  • Drive improvements in system architecture, balancing short‑term delivery with long‑term platform health.
  • Contribute to shaping the platform roadmap and technical direction in response to evolving business and ML needs.
• Cross‑Functional Collaboration
  • Partner closely with ML engineers, robotics teams, infrastructure teams, and product stakeholders to translate requirements into scalable platform solutions.
  • Act as a technical bridge between teams, ensuring platform capabilities align with real‑world use cases and constraints.
  • Influence platform adoption and best practices across multiple teams.
• Testing, Simulation & Validation
  • Support platform capabilities that enable simulation‑based testing and validation of ML systems, including synthetic data workflows.
  • Improve tooling that allows teams to test and…