Lead Engineer MLOps
Listed on 2026-07-25
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IT/Tech
Machine Learning/ ML Engineer, AI Engineer (Applied/Software)
Technical Lead Manager, Machine Learning Operations
Location: United States
Employment Type: Full-time
Focus: MLOps, ML Platform, Data Science Infrastructure, AI-Assisted Development, Supply Chain Technology
About Our Client
Our client is building modern logistics infrastructure for the future of ecommerce.
Their platform helps brands and consumers create a better post-purchase experience by making shopping, shipping, delivery, and returns more seamless. By combining next-generation technology with a vertically integrated logistics network, our client gives ecommerce brands more control over the customer delivery experience and helps turn delivery into an extension of the brand.
The company supports millions of deliveries and partners with some of the most recognized consumer brands in the market. Their culture is high-performance, merit-based, and built for people who want to compete, win, make an impact, and help build an enduring company.
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…
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