Software Engineer, MLOps - Machine Learning

Baton is Ryder ’s in-house product development group focused on harnessing emerging technologies to redefine transportation and logistics. With $10B in freight under management, our technology reaches every part of the U.S. economy.

We design and ship category-defining software that enables Ryder and its 50,000+ customers—including some of the world’s most well-known brands—to plan and execute freight intelligently, efficiently, and cost-effectively. Our work includes everything from customer-facing software to the data platform that will power the next era of innovation at Ryder.

Baton’s mission
: enable supply chain on autopilot.

Ryder acquired Baton in 2022 to power its next wave of digital products. We operate at startup speed, with Fortune 500 reach. If you have a passion for solving complex problems and creating impact for the engine of the American economy, you’ll love it here.

Role: Software Engineer, Machine Learning Operations

Job Type: Full Time

Work Model: Hybrid

Remote Days: Monday and Friday

Office Days: Tuesday, Wednesday, and Thursday

As a Software Engineer on Baton’s Machine Learning Pod, you will build and maintain the production infrastructure that supports the full machine-learning lifecycle. You will work across production software engineering, distributed systems, MLOps, and model development to help the team bring new models online and operate them reliably at scale.

Baton’s primary ML infrastructure is established, and the team is now building the next layer of MLOps capabilities on top of that foundation. You will help automate model monitoring, retraining, redeployment, experimentation, and drift detection as the number of production models continues to grow.

This is a hands-on individual contributor role for an engineer who can work across both infrastructure and modeling. You will build on the patterns and templates the team has already established, improve integration between the ML platform and Baton’s core transportation management platform, and make it easier for engineers to develop, ship, and maintain models end to end.

• Build and Expand MLOps Infrastructure:
  • Build automated capabilities for model monitoring, retraining, redeployment, champion/challenger testing, A/B testing, and drift detection.
  • Improve experiment tracking and model lifecycle management as the number of production models increases.
• Develop and Productionize Machine-Learning Models:
  • Bring new machine-learning models into production, including developing select models from initial concept through deployment.
  • Support models across development, deployment, monitoring, maintenance, and iteration.
  • Build scalable batch-prediction capabilities alongside real-time machine-learning workflows.
• Create Self-Serving ML Infrastructure:
  • Build on existing infrastructure patterns and templates to create reliable and reusable ML workflows.
  • Make it easier for engineers to ship and maintain models end to end with less manual intervention.
  • Improve development velocity while maintaining production reliability and operational quality.
• Design and maintain distributed systems that support data-intensive and machine-learning workloads.
• Improve the scalability, performance, and reliability of production ML infrastructure.
• Contribute to batch processing, caching, data movement, and cloud-native infrastructure.
• Connect ML Systems with Baton’s Core Platform:
  • Strengthen the integration between the ML platform and Baton’s core transportation management platform.
  • Replace manual integration workflows with scalable and maintainable infrastructure.
  • Enable machine-learning capabilities to support transportation workflows and operational…