Staff Machine Learning Engineer

Xometry is hiring a Staff Machine Learning Engineer (senior individual contributor) to lead end-to-end ML systems work for the DFM AI + IQE initiative and partner integrations.

Responsibilities

• Own the full ML lifecycle, from requirements gathering through release, delivering high-quality results on-time across complex, cross-functional initiatives
• Lead the partner integration AI/ML plane for the embedded DFM AI + IQE integration with Teamcenter and Designcenter
• Design real-time ML serving architecture and implement a low-latency signal path that delivers DFM and pricing feedback inside the designer environment
• Define data contracts for model inputs and outputs, and implement MLOps, governance, and observability for a mission-critical partner integration
• Develop cloud-based production systems for real-time endpoints and MLOps, integrated with Xometry’s broader systems and infrastructure
• Handle cross-domain technical complexity by evaluating variable factors and aligning solutions to both business and technical objectives
• Identify opportunities, drive new processes and solutions, and create multi-quarter roadmaps for key technical goals
• Apply best practices for automated testing, parallel and distributed computing, and secure software development across ML systems
• Collaborate with engineers, product managers, data scientists, and business stakeholders to translate requirements into robust technical solutions
• Conduct and support design reviews, code reviews, and technical mentorship to raise team capability
• Stay current with ML/AI advances and integrate relevant tools, frameworks, and approaches into production systems

Requirements

• Bachelor’s degree in a STEM field (or equivalent experience) plus 6-8 years of machine learning engineering experience
• Proven experience owning and delivering complex ML systems in production
• Deep expertise in ML and AI, 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 (AWS strongly preferred), including auto-scaling, monitoring, and alerting
• Strong proficiency in Python and advanced ML/AI frameworks such as TensorFlow or PyTorch
• Solid software engineering fundamentals, including data structures and algorithms
• Demonstrated MLOps experience: model monitoring, data and concept drift detection, and automated retraining and redeployment pipelines
• Experience with CI/CD (example: GitHub Actions), test driven development, and infrastructure as code (example: Terraform)
• Experience profiling and optimizing ML model deployments for latency and throughput
• Ability to operate independently on new and ambiguous assignments and communicate effectively across engineering, product, and business audiences
• Experience with state-of-the-art modeling techniques including transformers, self-supervised pre-training, large language models (LLMs), or generative AI
• Knowledge of containers, Kubernetes, and cloud-native distributed systems
• Background in manufacturing, supply chain, or marketplace environments is a plus

Location

• Denver, CO (hybrid)

Salary

• USD 200,000 - 220,000 per year

Technology Stack

• Python, TensorFlow, PyTorch
• Gradient Boosting, Deep Learning, Generative AI frameworks
• AWS, CI/CD pipelines, GitHub Actions, test driven development, Terraform (infrastructure as code)
• Transformers, self-supervised pre-training, large language models (LLMs)
• Containers, Kubernetes, cloud-native distributed systems
• Solid Edge, NX, Designcenter, Teamcenter
• MLOps, model monitoring, data drift detection, concept drift detection, automated retraining and redeployment pipelines

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