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Baton · 4 hours ago

Staff Software Engineer - Infrastructure, Machine Learning

San Francisco, California, United States

Full-time

Hybrid

Senior Level, Lead/Staff

$250K/yr - $330K/yr

5+ years exp

Baton is Ryder’s in-house product development group focused on harnessing emerging technologies to redefine transportation and logistics. As a Staff Software Engineer within the Machine Learning Team, you will tackle complex challenges in distributed systems and ML operations to enhance the machine learning infrastructure, building scalable systems that support model deployment and real-time inference.

Artificial Intelligence (AI)LogisticsSupply Chain Management

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

Qualification

Production PythonDistributed SystemsMachine Learning (MLOps)Data EngineeringTechnical LeadershipCross-Functional InfluenceLogistics ExperienceMentoring

Required

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

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

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