Staff Machine Learning Engineer

Staff Machine Learning Engineer

About

Xometry powers the industries of today and tomorrow by connecting people with big ideas to the manufacturers who can bring them to life. Our digital marketplace gives manufacturers critical resources to grow their business and makes it easy for buyers to tap into global manufacturing capacity.

Responsibilities

• Own the end-to-end lifecycle from requirements gathering through release, ensuring high-quality, on-time delivery across complex, cross-functional initiatives.
• Own the Partner integration AI/ML plane, architecting and building the high-performance AI/ML layer of Xometry’s embedded DFM AI + IQE integration with Teamcenter and Designcenter.
• Design the real-time ML serving architecture and the low-latency signal path for DFM and pricing feedback.
• Define data contracts for model inputs/outputs and implement the MLOps, governance, and observability required for a mission-critical integration.
• Develop cloud-based production systems powering real-time endpoints and MLOps, integrated with Xometry’s broader systems and infrastructure.
• Navigate complex, cross-domain technical challenges and deliver solutions that meet business and technical objectives.
• Proactively surface opportunity areas, take ownership of new processes and solutions, and develop multi-quarter roadmaps.
• Apply best practices in automated testing, parallel and distributed computing, and secure software development across ML systems.
• Partner with engineers, product managers, data scientists, and business stakeholders to translate requirements into robust technical solutions.
• Guide other engineers through design reviews, code reviews, and technical mentorship.
• Keep pace with advances in ML/AI and bring relevant new approaches, tools, and frameworks into practice.

Requirements

• Bachelor’s degree in a STEM field (or equivalent experience) plus 6-8 years of experience in machine learning engineering, with a track record of owning and delivering complex ML systems in production.
• Deep expertise in ML and AI technologies, including Gradient Boosting methods, Deep Learning, and/or Generative AI frameworks, with a focus on backend scalability and reusability.
• Hands-on experience deploying real-time ML products at scale in cloud environments (AWS strongly preferred), including auto-scaling, monitoring, and alerting.
• Strong proficiency in Python and advanced ML/AI frameworks such as TensorFlow, PyTorch, or similar.
• Solid grounding in software engineering fundamentals, data structures, and algorithms.
• Demonstrated experience with MLOps practices: model monitoring, data and concept drift detection, and automated retraining and redeployment pipelines.
• Proficiency with CI/CD pipelines, test-driven development, and infrastructure as code.
• Experience profiling and optimizing existing ML model deployments for latency and throughput.
• Ability to operate independently on new and ambiguous assignments.
• Experience with state-of-the-art modeling techniques including transformers, self-supervised pre-training, large language models (LLMs), or generative AI.
• Knowledge of containers, container orchestration (Kubernetes), and cloud-native distributed systems.
• Background in manufacturing, supply chain, or marketplace environments is a plus.