--- title: 'Software Engineer, MLOps - Machine Learning at Baton (A Ryder Technology Lab)' canonical: 'https://feeny.ai/job/software-engineer-mlops-machine-learning-baton-a-ryder-technology-lab-san-jzr23n2xsac8' type: 'job' last_seen: '2026-09-08' --- # Software Engineer, MLOps - Machine Learning at Baton (A Ryder Technology Lab) - **Company:** Baton (A Ryder Technology Lab) - **Location:** San Francisco California, United States - **Posted:** 2026-08-03 - **Last confirmed live:** 2026-09-08 - **Apply:** https://job-boards.greenhouse.io/baton/jobs/5200002007 ## Job description ## Who We Are [Baton](https://www.baton.io/) is [Ryder](https://www.ryder.com/en-us/about-us)’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. - 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. - Strengthen Distributed ML Systems: - 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 decision-making. - Collaborate Across the ML Lifecycle: - 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. Required Qualifications 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 and should be confirmed with Fabian as a must-have versus preferred qualification ## Preferred Qualifications - 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. The Perks - 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 Compensation Range: The annual base salary range for this position is $162,000 - $216,000* Compensation will vary based on factors including skill level, transferable knowledge, and experience. Note that the above is not the representation of total compensation, which includes our LTI Package as well. In addition to base salary, Baton's full-time employees are eligible for an annual company performance bonuses. ## Why You Should Join - 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. ## About Baton (A Ryder Technology Lab) ## Company Overview - **One-liner**: Baton is Ryder’s Silicon Valley-based technology lab that builds AI-powered software to digitize, optimize, and automate supply chain networks for some of the world’s largest brands. - **Entity Type**: Private (Acquired by Ryder System, Inc. / NYSE: R in 2022; operates as a wholly owned innovation lab) - **Headquarters**: San Francisco, California, United States - **Founded**: 2019 (as a startup); acquired 2022 - **Founders**: Andrew Berberick and Nate Robert ## Core Business - **Primary industry**: Transportation & Logistics Technology, Supply Chain Management - **Target customers**: B2B, Enterprise (Ryder’s 50,000+ customers, including Fortune 500 consumer goods, retail, and manufacturing brands) - **Mission/purpose statement**: "Enable supply chain on autopilot." ## Products & Services - **[RyderShare™](https://baton.io/)**: A real-time collaborative logistics platform that tracks shipments, manages exceptions, and connects supply chain stakeholders through a single-pane view. Includes proactive notifications and full audit trails. - **AI-Powered Optimization Engine**: A first-of-its-kind platform designed to combat waste in transportation networks by improving routing, scheduling, and dock detention, specifically built to handle seasonality and fluctuating demand. - **Data Platform & Infrastructure**: The underlying data systems that power the next generation of innovation at Ryder, enabling intelligent planning and execution across the entire fleet. ## Market Standing - **Valuation/Market Cap**: Not publicly available for the lab itself. Parent company Ryder System, Inc. reported over $12 billion in annual revenue and has invested $1.3 billion in technology over five years. - **Key Metric**: Ryder manages **$10B in freight under management**, operates a fleet of **260K+ vehicles**, and Baton’s platform processes **10M shipment loads daily**. - **Notable Investors/Partners**: Originally backed by 8VC and Maersk Growth (Series A). Now operates fully under Ryder System, Inc. - **Growth Signals**: Active hiring (8 open roles in Q3 2024), headcount growing 22% YoY, 120+ RyderShare customers, and a clear mandate from Ryder leadership to prepare the company for the coming AI wave. ## Competitive Advantages - **Startup speed with Fortune 500 strength**: Teams fully own their roadmap and ship at startup velocity, but with the resources, data, and stability of a $12B industry giant. - **Unmatched data moat**: Access to the largest full-service commercial truck fleet in the U.S. and a decade of supply chain data provides a training ground for AI models that no pure-play startup can match. - **Elite engineering talent**: The team includes engineers from NASA’s Jet Propulsion Laboratory (led the Mars Rover program), Apple, Meta, OpenAI, Tesla, and Google. - **FreightTech credibility**: Previously ranked #4 in FreightWaves’ FreightTech 25 (ahead of Uber Freight and Tesla). ## Strategic Focus - **AI-first platform**: Baton’s primary mandate is to build and deploy a new generation of AI-powered digital platforms and optimization engines for Ryder’s customers. - **Network digitization**: Continue expanding RyderShare’s capabilities to connect more shippers, carriers, and warehouses, eliminating waste and reducing detention. - **Talent acquisition**: Actively recruiting the brightest software engineers, product managers, and data scientists from top-tier tech companies to solve the hardest problems in logistics. ## Why Work Here - **Best of both worlds**: "Operate at startup speed, with Fortune 500 reach" — rare autonomy and impact without the risk of a pre-revenue startup. - **Hard problems, massive scale**: Work on challenges that directly affect the engine of the American economy, with a dataset covering millions of daily shipments. - **High-caliber peers**: Work alongside former NASA engineers, ex-OpenAI researchers, and alumni from Apple, Meta, Google, and Tesla. - **Culture of curiosity**: The team describes itself as people with a "shared curiosity for how things move through the world." - **Hybrid/In-office**: Based in San Francisco (Silicon Valley) with a strong in-person collaboration ethos. - **Career growth**: Direct ownership of product roadmaps and the chance to shape a brand-new lab that is central to Ryder’s digital transformation strategy. ## Sources 1. [baton.io](https://baton.io/) 2. [Greenhouse Careers Page](https://job-boards.greenhouse.io/baton) 3. [LinkedIn Company Profile](https://www.linkedin.com/company/baton-trucking) 4. [Ryder Newsroom - Lab Announcement (BusinessWire)](https://newsroom.ryder.com/news/news-details/2023/Ryder-Establishes-Silicon-Valley-Based-Technology-Lab-Led-by-Founders-of-Start-Up-Baton/default.aspx) 5. [Ryder Investor Relations - Same Announcement](https://investors.ryder.com/news-events/News-Releases/news-details/2023/Ryder-Establishes-Silicon-Valley-Based-Technology-Lab-Led-by-Founders-of-Start-Up-Baton/default.aspx) ## Other roles at Baton (A Ryder Technology Lab) - [Senior Product Manager, Machine Learning](https://feeny.ai/job/senior-product-manager-machine-learning-baton-a-ryder-technology-lab-san-vvxsjnss8f5w) — San Francisco California, United States - [Senior Product Manager](https://feeny.ai/job/senior-product-manager-baton-a-ryder-technology-lab-san-francisco-california-3rkg15qma0jj) — San Francisco California, United States - [Staff Software Engineer - Infrastructure, Data Platform](https://feeny.ai/job/staff-software-engineer-infrastructure-data-platform-baton-a-ryder-technology-t0q0phjnvbw1) — San Francisco California, United States - [Software Engineer - Data Platform](https://feeny.ai/job/software-engineer-data-platform-baton-a-ryder-technology-lab-san-francisco-j0r2vthv845t) — San Francisco California, United States - [Senior Software Engineer - Full Stack, Technical Lead](https://feeny.ai/job/senior-software-engineer-full-stack-technical-lead-baton-a-ryder-technology-lab-k2cb3dafyh9r) — San Francisco California, United States - [Software Engineer - Infrastructure, Data Platform](https://feeny.ai/job/software-engineer-infrastructure-data-platform-baton-a-ryder-technology-lab-san-nzxmcrpfdp51) — San Francisco California, United States