Software Engineer, MLOps - Machine Learning

Responsibilities

Build and Expand MLOps Infrastructure

• 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.

Develop and Productionize Machine-Learning Models

• 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.

Create Self-Serving ML Infrastructure

• 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.

Strengthen Distributed ML Systems

• 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.

Connect ML Systems with the Core Platform

• Strengthen the integration between the ML platform and the 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.

Collaborate Across the ML Lifecycle

• 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 team needs.

Requirements

• Advanced proficiency coding 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.
• Strong background in distributed computing, scalable ML infrastructure, and high-performance engineering.
• Experience building or maintaining systems that support data-intensive and machine-learning workloads.
• Familiarity with big-data systems, batch processing, caching, and cloud infrastructure.
• 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.

Nice-to-Haves

• Experience implementing, deploying, monitoring, and maintaining machine-learning models in production.
• 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 integrating ML platforms with broader production or operational systems.
• 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.

Compensation and Benefits

• Annual base salary range: $162,000–$216,000.
• Annual company bonus and cash bonus structure.
• Long-term incentive plan.
• 401(k) with matching.
• Hybrid work schedule.
• Medical, dental, and vision coverage.
• Employee stock purchase program with a 15% discount to market value.
• Collaborative, tech-forward office in Hayes Valley, San Francisco.