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: Staff Software Engineer - Infrastructure
Team: Machine Learning Pod
Location: Hayes Valley, San Francisco, CA
Basic Job Details
Job Type: Full Time
Work Model: Hybrid
Remote Days: Monday & Friday
Office Days: Tuesday, Wednesday, Thursday
Job Description
As a Staff Software Engineer within our Machine Learning Team, you will tackle complex challenges in distributed systems and ML operations to enhance our machine learning infrastructure. You’ll build scalable ML infrastructure from the ground up - supporting model deployment, distributed training, real-time inference, and more. You’ll be a key partner to the Data Science team, helping bring value to production quickly and reliably. This role requires a blend of advanced Python programming skills within production environments and expertise in distributed computing.
Responsibilities
- Own Core ML Infrastructure:
- Build and scale distributed systems for ML training, serving, and inference.
- Design and implement real-time ML workflows that power core product features.
- Implementation of Distributed Systems:
- Build robust distributed systems tailored for efficient ML training and seamless operational deployment.
- Feature Engineering Enhancement:
- Streamline and manage both online and offline feature stores, optimizing feature engineering processes for greater efficiency.
- Real-Time ML Workflow Enhancement:
- Improve real-time machine learning workflows to support dynamic decision-making and automate core operational processes.
- Platform Level Ownership:
- Lead the development of ML Ops systems, including model deployment, monitoring, and experiment tracking.
- Architect and manage scalable feature stores for online and offline usage.
- AI-Driven Optimization:
- Contribute to agentic AI systems for freight matching, ETA prediction, and load scheduling.
- Support systems that improve Stop Estimation Accuracy and Cross-Mode Optimization.
- Production Ready Engineering:
- Write production-grade Python that operates at scale, with reliability and performance top of mind.
- Collaborate across engineering and data science to turn models into resilient software systems.
Required Qualifications
- Production Python & Distributed Systems Expertise
- Advanced proficiency in Python at a Staff Level
- Must be within a production environment where the code directly impacts operations.
- Experience in distributed computing, scalable ML infrastructure, & high-performance engineering.
- Machine Learning (MLOps)
- Scales ML infra for multiple teams and use cases.
- Experience implementing and serving ML algorithms.
- Ensures reproducibility, lineage, and experiment rigor.
- Owns end-to-end ML systems: training, deployment, features, monitoring, rollback.
- Hands-on experience with data engineering, distributed training, model monitoring, and experiment tracking.
- Breadth of knowledge and applied experience across multiple ML applications, with proven ability to leverage a wide range of tools, frameworks, and systems.
- Technical Leadership & Cross-Functional Influence
- Leads design and delivery of large-scale ML or distributed systems.
- Defines reusable patterns, standards, and architectures.
- Drives decisions that improve reliability, latency, and developer velocity.
- Sets technical direction and elevates ML engineering standards.
- Communicates vision and trade-offs across disciplines.
- Can Mentor other ML engineers on the team.
Preferred Qualifications
- 5 to 8 years of backend or ML infrastructure experience.
- Proven track record building production ML workflows at scale.
- Experience in industry logistics, transportation, or freight is a bonus.
The Perks
- Competitive Base Salary
- Long Term Cash Incentive Plans
- Annual Company Bonus
- 401k with Matching
- Hybrid Work Schedule
- Comprehensive Health Coverage
- Hyper-Stable, publicly traded Enterprise
- Employee Stock Purchase Program (15% discount to market value)
- Collaborative, Tech-Forward, Cozy office environment in Hayes Valley
Compensation Range: The annual base salary range for this position is $250,000 - $330,000*
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.
