Software Engineer, MLOps - Machine Learning

About the role

Who We Are

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

Pod: Machine Learning

Location: Hayes Valley, San Francisco, CA

Basic Job Details

Job Type: Full Time Work Model: Hybrid Remote Days: Monday and Friday Office Days: Tuesday, Wednesday, and Thursday

Job Description

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.

Responsibilities

- 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.