Blue River Technology

Full-stack Engineer

• Ensure software architecture enhances maintainability
• Champion robust simulation environments
• Work with Product Management to deliver value
• Recruit and mentor software engineering teams

Director, Software Engineering

Platform Engineer

• 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.
• 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.
• 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.
• 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.
• 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.
• 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 validate models before production deployment.
• Provide technical guidance and mentorship to junior engineers on platform and systems design.
• Lead implementation efforts for key platform initiatives and ensure high-quality execution.
• Demonstrate strong ownership and accountability for delivering impactful platform improvements.

Director, Software Engineering

Senior Platform Engineer, CVML