Technical Lead Manager, Machine Learning OperationsLocation: United States
Employment Type: Full-time
Focus: MLOps, ML Platform, Data Science Infrastructure, AI-Assisted Development, Supply Chain Technology
About the Role
Our client is hiring a
Technical Lead Manager, Machine Learning Operations to own the Data Science platform and lead the roadmap for building a more sophisticated, stable, and scalable ML infrastructure foundation.
This person will lead a team focused on ML infrastructure, ML operations, and embedded data science engineering. The team partners closely with data scientists to ensure forecasting, network orchestration, pricing, routing, and other machine learning systems are well-designed, production-ready, and built to scale.
This is a hands-on leadership role. You'll manage and grow the team while still contributing technically through architecture, code, design reviews, roadmap ownership, and setting the engineering bar.
What You'll Do
- Lead and grow a team of engineers across ML infrastructure, MLOps, and embedded data science project work
- Own the 1-2 year roadmap for improving the company's ML platform and operations research infrastructure
- Standardize and improve training infrastructure, serving infrastructure, deployment pipelines, monitoring, permissions, environments, and service operations
- Embed engineers into major science initiatives across forecasting, network orchestration, pricing, routing, and supply chain optimization
- Help ensure data science projects are production-ready from day one
- Build templates, patterns, and platform standards that help new ML systems get up and running quickly
- Partner closely with data science, engineering, developer experience, and platform teams
- Drive adoption of AI-assisted and agentic development workflows across the Data Science organization
- Set standards for using AI in EDA, model iteration, ML/OR methodology, and development velocity
- Review designs, write code, improve technical quality, and raise the bar for production ML systems
- Participate in the on-call rotation for production data science systems
What We're Looking For
- Bachelor's degree with 6+ years of Machine Learning Engineering experience, or Master's degree with 4+ years of Machine Learning Engineering experience
- Experience leading or managing high-velocity ML platform, MLOps, or ML infrastructure teams
- Strong hands-on experience building production ML systems
- Experience with ML platforms, including training infrastructure, serving infrastructure, feature stores, orchestration, monitoring, and deployment pipelines
- Strong Python experience
- Experience driving AI-assisted or agentic tooling adoption inside an engineering or data science organization
- Strong knowledge of cloud-based data engineering and data science tools, preferably AWS
- Experience with data warehouses such as Redshift, Databricks, Snowflake, or similar platforms
- Experience with open-source large-scale ML tooling such as Ray, Flink, Feast, or similar technologies
- Ability to balance short-term business impact with long-term platform vision
- Strong communication skills and a business-value-first approach to technical work
Bonus Experience
- Experience building ML systems in logistics, ecommerce, supply chain, transportation, marketplaces, or operations-heavy businesses
- Experience supporting forecasting, routing, pricing, network optimization, or operations research systems
- Experience partnering directly with data science teams to productionize models
- Experience building reusable ML templates, internal platforms, or service creation frameworks
- Experience improving developer experience or AI-assisted development workflows
Why This Opportunity
- Lead the platform foundation behind high-impact data science systems
- Work on machine learning problems tied directly to real-world logistics, delivery, pricing, forecasting, and network orchestration
- Manage and grow a technical team while staying hands-on
- Own a meaningful roadmap for ML infrastructure at scale
- Help drive AI-assisted development adoption across a data science organization
- Build systems that power millions of package decisions and help major ecommerce brands deliver better customer experiences
- Join a high-performance team with strong growth potential and meaningful equity upside
