Staff Machine Learning Engineer

About the Role

Xometry is looking for a Staff Machine Learning Engineer to join our growing AI/ML team. This is a senior individual contributor role with broad technical scope and meaningful organizational impact. You will lead the design and delivery of complex ML systems, architect integrations across our tech stack, and set the engineering standard for how we build and deploy machine learning solutions at scale.

A defining piece of this role is owning the AI/ML architecture behind the DFM AI + IQE integration. You will serve as the data engineering lead for the digital thread connecting Xometry's platform to our partner ecosystem (Solid Edge, NX, Designcenter, and Teamcenter), building the pipelines, contracts, and observability that move quotes, parts, manufacturability signals, and pricing between the systems in real time.

Responsibilities

• Lead with technical depth: 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: Architect and build the high-performance AI/ML layer of Xometry's embedded DFM AI + IQE integration with Teamcenter and Designcenter. Design real-time ML serving architecture and low-latency signal paths.
• Build for scale: Develop cloud-based production systems powering real-time endpoints and MLOps integrated with Xometry's broader systems and infrastructure.
• Solve ambiguous problems: Navigate complex, cross-domain technical challenges and evaluate variable factors to meet business and technical objectives.
• Set the standard: Proactively identify opportunity areas, take ownership of new processes and solutions, and develop multi-quarter technical roadmaps.
• Champion quality and security: Apply best practices in automated testing, parallel/distributed computing, and secure software development across ML systems.
• Collaborate broadly: Partner with engineers, product managers, data scientists, and business stakeholders.
• Mentor and elevate: Guide other engineers through design reviews, code reviews, and technical mentorship.

Qualifications

• Bachelor's degree in a STEM field (or equivalent experience) plus 6–8 years of experience in machine learning engineering.
• 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.
• 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 or PyTorch.
• 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/redeployment pipelines.
• Proficiency with CI/CD pipelines (e.g., GitHub Actions), test-driven development, and infrastructure as code (e.g., Terraform).
• Experience profiling and optimizing existing ML model deployments for latency and throughput.
• 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.