Ryder's in-house product development group, Baton, is looking to solve complex problems in transportation and logistics by harnessing emerging technologies, with a mission to enable supply chain on autopilot.
Requirements
- Production Python & Distributed Systems Expertise: Advanced proficiency in Python at a Staff Level, 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.
- Technical Leadership & Cross-Functional Influence: Leads design and delivery of large-scale ML or distributed systems. Defines reusable patterns, standards, and architectures.
- 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.
- Experience in industry logistics, transportation, or freight is a bonus.
- Proven track record building production ML workflows at scale.
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.
Other
- 5 to 8 years of backend or ML infrastructure experience.
- Competitive Base Salary
- Long Term Cash Incentive Plans
- Annual Company Bonus
- 401k with Matching
- Hybrid Work Schedule
- Comprehensive Health Coverage
