Sr Platform Engineer, CVML

Blue River Technology

10d•$160,000 - $287,000•Remote

About The Position

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. Summary We are seeking a Senior CVML Platform Engineer to help design, build, and evolve the platforms that support computer vision and ML workloads at scale. This 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 DevOps 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.

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.

Nice To Haves

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.

Responsibilities

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.