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About the Role

Baton is Ryder’s in-house product development group focused on harnessing emerging technologies to redefine transportation and logistics. We design and ship category-defining software that enables Ryder and its 50,000+ customers to plan and execute freight intelligently, efficiently, and cost-effectively. Baton’s mission is to enable supply chain on autopilot. We operate at startup speed with Fortune 500 reach.

Responsibilities

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.
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.
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.
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 decision-making.
Partner with engineers and cross-functional stakeholders to identify opportunities for automation and model productionization.
Contribute across software engineering, ML development, infrastructure, and production operations based on the needs of the team.

Requirements

Production Python Expertise
Advanced proficiency coding in production-grade Python at an L4 or L5 level
Experience working in an environment where production code directly impacts operations
Ability to build and maintain reliable software across modeling, infrastructure, and automation workflows
Distributed Systems Expertise
Strong background in distributed computing, scalable ML infrastructure, and high-performance engineering
Experience building or maintaining systems that support data-intensive and ML workloads
Familiarity with big-data systems, batch processing, caching, and cloud infrastructure
Machine Learning / MLOps
Experience implementing, deploying, and productionizing machine-learning algorithms
Hands-on experience with data engineering, distributed training, model monitoring, and experiment tracking
Experience with model retraining, redeployment, serving, and lifecycle management
Strong SQL knowledge and caching experience
Experience with model lifecycle platforms such as SageMaker is a plus
Experience implementing, deploying, monitoring, and maintaining machine-learning models in production.
Experience with Kubernetes and cloud infrastructure, preferably AWS.
Familiarity with ML and data technologies such as Kubeflow, Iceberg, Feast, or SageMaker.
Experience with batch prediction, model serving, distributed training, experiment tracking, caching, or feature stores.
Experience building scalable, self-serving infrastructure for machine-learning teams.
Experience integrating ML platforms with broader production or operational systems.
Previous experience in a technically rigorous environment such as a large-scale technology company, infrastructure organization, or high-growth engineering team.
Experience in logistics, transportation, freight, or supply chain is a plus but not required.

Skills

Python
Distributed Systems
MLOps
Machine Learning
SQL
Kubernetes
AWS
Kubeflow
Iceberg
Feast
SageMaker
Batch Prediction
Model Serving
Distributed Training
Experiment Tracking
Caching
Feature Stores

Location

Hayes Valley, San Francisco, CA

Work Type

Full Time
Hybrid

Experience Level

L4 or L5 level

Salary/Compensations

$162,000 - $216,000

Benefits

Competitive Base Salary + Cash Bonus Structure
Annual Company Bonus + Long Term Incentive Plan
401(k) with Matching
Hybrid Work Schedule
Hyper-Stable, Publicly Traded Enterprise
Medical, Dental, and Vision Health Coverage
Employee Stock Purchase Program with a 15% Discount to Market Value
Collaborative, Fun, and Tech-Forward Office in Hayes Valley, San Francisco

About the Company

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.
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.
Have an immediate impact: With Ryder’s existing customer base of 50,000+ companies and an internal headcount of 43,000, the scale and impact of our products will be large and far-reaching, from day one.
Opportunity to grow and lead in a Fortune 500 company: You’ll get to work in a rapidly growing, startup-like environment while having the stability and backing of Ryder and its full executive team.
Creative, fast-paced environment to solve impactful problems in Supply Chain: We’re going to design completely new tools for an industry that hasn’t been rethought in decades. And to do this, we need people who think differently.