Technical Lead Manager, MLOps

About the role

Veho’s Data Science team is core to Veho’s ability to deliver millions of packages by creating the systems that drive forecasting, network orchestration, pricing, and routing decisions. This role involves owning our Data Science platform and our 1-2 year roadmap for creating a sophisticated and stable platform that keeps up with Veho’s rapid growth.

What You’ll Do

• Lead and grow a team of four engineers spanning ML infrastructure, ML operations, and embedded data science project work.
• Improve our internal ML platform: standardize and improve ML infrastructure, improve how DS services are created, deployed, and operated. Think service performance, permissioning, environment setup, and integration with upstream and downstream systems.
• Set the roadmap for improving our Machine Learning and Operations Research infrastructure.
• Embed engineers into major science initiatives (forecasting, network orchestration, pricing) so every project is technically sound and lessons learned find their way back into our platform.
• Drive AI usage across DS. Collaborate with our Agentic Developer Experience team to ensure new tooling has a high impact on the Data Science team’s velocity. Set standards, introduce patterns, and drive adoption of how to leverage AI in data science workflows (EDA, model iteration, ML/OR methodologies).
• Be part of the on-call rotation for our data science production systems.

What You Bring

• Bachelor’s Degree plus at least 6 years of experience in Machine Learning Engineering, or Master’s Degree plus at least 4 years in Machine Learning Engineering:
• ML platform experience: training and serving infrastructure, feature stores, orchestration, monitoring, deployment pipelines
• Experience managing impactful, high velocity ML Platform / ML Ops teams in smaller scale companies
• Experience driving AI/agentic tooling adoption inside an organization
• Hands-on experience with open-source tooling for large-scale ML (e.g., Ray, Flink, Feast)
• Strong knowledge of Cloud-based data engineering and data science tools (AWS preferred) and Data Warehouses (Redshift, Databricks, Snowflake)
• Strong proficiency in Python
• Interest in building systems in a Supply Chain setting, enabling a physical supply chain to run like clockwork