Technical Lead Manager, Machine Learning Operations
Veho
Software Engineering, IT, Operations, Data Science
California, USA · Philadelphia County, PA, USA · San Francisco, CA, USA · Texas, USA · New York, USA · Boston, MA, USA · Dallas, TX, USA
About Veho
About The Role:
- Is an expert in their craft, creating high quality ML infrastructure and delivering impactful machine learning models to our stakeholders.
- Works in close collaboration with the other Data Science team members and keeps the business value at the center of their work. Has a bias for action, balancing delivering impact in the short-term while building out the long term vision.
- Applies their ML / MLOPS knowledge to suggest new patterns, tools, approaches to improve the team’s models
- Drives team velocity by helping the current team develop in their careers, hiring strong new talent onto the team, and adopting AI as a core part of development.
- 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.
- 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:
- This experience should include:
- 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.