Senior Platform Engineer, CVML

Role Overview

• 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.

Requirements

• 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.

Tech Stack

• AWS
• Cloud
• Kubernetes
• Python
• Ray

Benefits

• Visa sponsorship will be considered on a case-by-case basis.