Senior MLOps Engineer

Your Responsibilities

As a Senior MLOps Engineer for John Deere Intelligent Solutions Group located in Santa Clara, CA, you will:

• Develop and maintain the infrastructure, workflows, and operational practices that support machine learning across the perception and autonomy stack.
• Build scalable, reliable processes for moving data from collection through curation, training, evaluation, deployment, and monitoring.
• Partner with perception, robotics, systems, and platform engineers to make ML development faster, more reproducible, and easier to operate in production.
• Create and improve tools that help the team understand model behavior, diagnose failures, compare experiments, and track quality over time.
• Support production deployment of ML capabilities by improving automation, validation, release processes, and operational visibility.
• Help define and maintain practical standards for ML workflows, including data management, experiment tracking, evaluation, testing, and model lifecycle management.
• Identify bottlenecks and reliability issues in existing ML and data workflows, then propose pragmatic improvements that increase team velocity and confidence.
• Collaborate across teams to ensure ML systems integrate cleanly with broader robotics, cloud, data, and software infrastructure.
• Contribute to a strong engineering culture through thoughtful design discussions, code reviews, documentation, and shared best practices.

VISA Sponsorship is NOT available for this position

What Skills You Need

• 3+ years of professional experience building, operating, or supporting software systems used by engineering teams.
• Working knowledge of machine learning workflows and the practical challenges involved in developing, evaluating, deploying, or maintaining ML-based systems.
• Ability to design reliable, maintainable tools and infrastructure that improve developer productivity and system quality.
• Comfort working across boundaries between software engineering, data, machine learning, cloud infrastructure, and production systems.
• Strong debugging, problem-solving, and communication skills in ambiguous technical environments.
• Demonstrated ability to collaborate effectively with multidisciplinary teams and drive practical improvements to shared engineering workflows.

What Makes You Stand Out

• Experience supporting ML or data workflows at scale, including training, evaluation, deployment, or monitoring.
• Experience optimizing and deploying trained models on compute-limited edge devices.
• Familiarity with cloud infrastructure, distributed compute, containerized environments, or workflow orchestration.
• Experience with data pipelines, dataset management, experiment tracking, model registries, or related ML lifecycle tools.
• Background working with robotics, autonomy, perception, sensor data, or other systems that combine ML with real-world operational constraints.
• Comfort improving reliability, observability, automation, and repeatability in complex engineering systems.
• Experience enabling teams through internal tools, platforms, documentation, or shared engineering practices.
• Interest in building pragmatic infrastructure that supports applied ML in production rather than focusing solely on research novelty

Education

Ideally, you will have a degree or equivalent related work experience in the following:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Robotics, Data Science, or a related technical field.
• Equivalent professional experience will be considered in lieu of a formal degree.
• Coursework or demonstrated expertise in software engineering, machine learning systems, data infrastructure, or robotics is a plus.