Senior CVML 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

• 5+ years of professional engineering experience, with a focus on platform, infrastructure, or systems engineering
• Strong technical judgment, balancing the evolution of legacy platforms with the design and delivery of new, greenfield components shared across multiple teams and workloads
• Excellent Python skills, used in production systems, tooling, and platform components
• Solid understanding of ML systems and the end-to-end model development lifecycle, from experimentation to deployment and iteration
• Hands-on experience or strong familiarity with cloud platforms (AWS preferred) and container orchestration systems such as Kubernetes and Slurm
• Ability to partner effectively with ML engineers, infra teams, and product stakeholders to translate requirements into platform capabilities
• Ability to quickly ramp up on new domains, tools, and complex existing systems

• Golang experience, particularly for platform or infrastructure components
• Experience building or integrating ML pipelines using tools such as Kubeflow and/or Airflow
• Understanding of model inference architectures, including performance, scalability, reliability, and cost considerations
• Experience enabling distributed training and inference through platforms and frameworks such as Ray
• Experience supporting ML systems in computer vision or robotics environments

bonus and benefit programs