Senior Manager, ML Engineering

Xometry (NASDAQ : XMTR) powers the industries of today and tomorrow by connecting the people with big ideas to the manufacturers who can bring them to life. Xometry's digital marketplace gives manufacturers the critical resources they need to grow their business while also making it easy for buyers at Fortune 1000 companies to tap into global manufacturing capacity.

We are seeking a highly skilled Senior Manager of Machine Learning Engineering with a strong background in ML model productionization. This role oversees ML Engineering efforts to productionize and deploy ML models that predict the cost to manufacture customers' parts and determine the best price to charge for them. This is at the core of our business with substantial opportunity for impact by solving interesting and challenging technical problems.

The ideal candidate will have at least 8+ years of total experience in the industry, including a minimum of 3 years in a leadership role, and experience with pricing algorithms and deep learning.

This position requires both leadership and hands-on technical expertise, managing a team of engineers while actively contributing to the design, development, and deployment of machine learning models and systems. Our teams are highly collaborative, cross-functional, and rapidly iterating and innovating.

Key Responsibilities :

• Lead, mentor, and manage a team of machine learning engineers, providing guidance on best practices in ML Ops, infrastructure, and software engineering.
• Lead the productionization of ML models and their deployment to quickly iterate on ML at the core of our business.
• Be hands-on in the design, development, and deployment of machine learning models and systems, ensuring they meet high standards of performance, scalability, and reliability.
• Collaborate with data scientists, product managers, software engineers, and other stakeholders to define project requirements and deliverables.
• Develop and maintain ML Ops pipelines, ensuring efficient model training, deployment, and monitoring.
• Implement and manage infrastructure for large-scale data processing, model training, and inference.
• Drive continuous improvement in engineering practices, including code quality, testing, and deployment automation.
• Stay up-to-date with the latest trends and advancements in machine learning, software engineering, and cloud infrastructure to inform team strategy and direction.
• Manage project timelines, resources, and deliverables, ensuring projects are completed on time and within budget.
• Foster a culture of innovation, collaboration, and continuous learning within the engineering team.

Qualifications :

Preferred Qualifications :

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