Staff Machine Learning Engineer

Xometry’s AI/ML team is building advanced machine learning systems that connect design, manufacturing, and partner tooling in a way that needs to run reliably in production. In this Staff Machine Learning Engineer role (hybrid in Lexington, KY), you will lead end-to-end delivery of complex ML systems, including the AI/ML architecture supporting Xometry’s DFM AI + IQE integration.

You will focus on real-time ML serving and low-latency signal pipelines that bring DFM and pricing feedback directly into the designer’s environment, while also owning the integration layer that powers partner ecosystem interoperability with Solid Edge, NX, Designcenter, and Teamcenter.

Responsibilities

• Lead with technical depth by owning the full lifecycle from requirements 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 that delivers DFM and pricing feedback to the designer’s environment
• Define data contracts for model inputs and outputs, and implement MLOps, governance, and observability required for a mission-critical, public-marketplace partner integration
• Develop cloud-based production systems for real-time endpoints and MLOps, integrated with Xometry’s broader systems and infrastructure
• Navigate complex, cross-domain technical challenges by evaluating variable factors and aligning solutions with both business and technical objectives
• Surface opportunities, take ownership of new processes and solutions, and build multi-quarter roadmaps for key technical objectives
• 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 to raise overall team capability
• Keep pace with advances in ML/AI and bring relevant 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 record of owning and delivering complex ML systems in production
• Deep expertise in ML and AI technologies, including Gradient Boosting, 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, with 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, including 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 (for example, GitHub Actions), test driven development, and infrastructure as code (for example, Terraform)
• Experience profiling and optimizing existing ML deployments for latency and throughput
• Ability to work independently on new and ambiguous assignments, determine methods and procedures, and communicate effectively across engineering, product, and business audiences
• Experience with state-of-the-art modeling techniques such as transformers, self-supervised pre-training, large language models (LLMs), or other generative AI
• Knowledge of containers, Kubernetes, and cloud-native distributed systems
• Background in manufacturing, supply chain, or marketplace environments is a plus (curiosity and drive matter)

Technologies

• Python; TensorFlow; PyTorch
• Gradient Boosting; Deep Learning; Generative AI frameworks
• AWS
• CI/CD pipelines; GitHub Actions; test driven development; Terraform
• MLOps; model monitoring; data and concept drift detection; auto-scaling
• Containers; Kubernetes; parallel and distributed computing
• Transformers; self-supervised pre-training; large language models (LLMs)
• Solid Edge; NX; Designcenter; Teamcenter
• Teamcenter and Designcenter

Benefits

• 401(k) match
• Medical, dental and vision insurance
• Life and disability insurance
• Generous paid time off including vacation, sick leave, floating and fixed holidays
• Maternity and bonding leave
• EAP, other wellbeing resources