Staff Machine Learning Engineer

Join Xometry’s AI/ML team in Lexington, KY (hybrid) as a Staff Machine Learning Engineer, leading end-to-end delivery of complex ML systems and partner integrations.

Responsibilities

• Technical ownership across the lifecycle: lead requirements gathering through release, delivering high-quality results on time across complex, cross-functional initiatives
• Partner integration AI/ML architecture: architect and build the high-performance AI/ML layer of the embedded DFM AI + IQE integration with Teamcenter and Designcenter
• Real-time ML serving: design serving architecture and a low-latency signal path to deliver DFM and pricing feedback directly into the designer’s environment
• Define ML data contracts: specify model input/output contracts and implement MLOps, governance, and observability for a mission-critical, public-marketplace partner integration
• Cloud production systems: develop cloud-based systems for real-time endpoints and MLOps, integrated with Xometry’s broader systems and infrastructure
• Cross-domain problem solving: evaluate variable factors and deliver solutions aligned with both business and technical objectives
• Roadmapping and process improvement: identify opportunity areas, own new processes and solutions, and develop multi-quarter roadmaps for key technical objectives
• Engineering quality and security: apply best practices in automated testing, parallel and distributed computing, and secure software development for ML systems
• Collaborate across functions: work with engineers, product managers, data scientists, and business stakeholders to translate requirements into robust solutions
• Mentorship and reviews: conduct design reviews, code reviews, and technical mentorship to raise team capability
• Stay current: keep pace with ML/AI advances 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 machine learning engineering experience
• Production ML ownership: proven track record owning and delivering complex ML systems in production
• ML/AI expertise: deep knowledge of ML and AI technologies, including Gradient Boosting and Deep Learning and/or Generative AI frameworks, with a focus on backend scalability and reusability
• Real-time ML at scale: hands-on experience deploying real-time ML products in cloud environments; AWS strongly preferred, including monitoring, alerting, and auto-scaling
• Python and frameworks: strong proficiency in Python and advanced ML/AI frameworks such as TensorFlow or PyTorch
• Software engineering fundamentals: solid grounding in data structures and algorithms
• MLOps experience: model monitoring, data and concept drift detection, and automated retraining and redeployment pipelines
• Delivery and infrastructure practices: CI/CD pipelines (e.g., GitHub Actions), test-driven development, and infrastructure as code (e.g., Terraform)
• Performance optimization: experience profiling and optimizing ML deployments for latency and throughput
• Independent execution: ability to operate on new and ambiguous assignments, determine methods and procedures, and communicate effectively across engineering, product, and business audiences
• Modern modeling exposure: transformers, self-supervised pre-training, LLMs, or generative AI experience
• Cloud-native systems: knowledge of containers, Kubernetes, and cloud-native distributed systems
• Domain experience: manufacturing, supply chain, or marketplace background is a plus

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
• Transformers
• Self-supervised pre-training
• Large language models (LLMs)
• Solid Edge
• NX
• Designcenter
• Teamcenter
• Parallel and distributed computing

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 and other wellbeing resources