Software Engineer, MLOps - Machine Learning

Sourced from feeny.ai

Baton, Ryder's in-house product development group, is hiring a Software Engineer on its Machine Learning Pod to build and maintain production infrastructure across the full ML lifecycle. You will work across production software engineering, distributed systems, MLOps, and model development to bring models online and operate them reliably at scale. The role focuses on automating model monitoring, retraining, redeployment, experimentation, and drift detection while integrating the ML platform with Baton's core transportation management platform.

• 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 production models scale
• Bring new machine-learning models into production from concept through deployment
• Build scalable batch-prediction capabilities alongside real-time ML workflows
• Create reliable, reusable, self-serving ML infrastructure and workflows
• Design and maintain distributed systems for data-intensive ML workloads
• Integrate ML systems with Baton's core transportation management platform
• Collaborate with engineers and stakeholders to identify automation and productionization opportunities

• Advanced proficiency coding in production-grade Python at an L4 or L5 level
• Strong background in distributed computing, scalable ML infrastructure, and high-performance engineering
• 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
• Experience with Kubernetes and cloud infrastructure, preferably AWS
• Familiarity with 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 in logistics, transportation, freight, or supply chain