Software Engineer, MLOps - Machine Learning

Baton•San Francisco, CA

4d•Hybrid

About The Position

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.

Requirements

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

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.
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.