Staff Machine Learning Engineer

Lead end-to-end machine learning and integration work for Xometry’s DFM AI + IQE initiative, building real-time, low-latency pipelines and production MLOps for partner environments.

Responsibilities

• Own the full ML lifecycle from requirements to release, delivering high-quality outcomes on schedule across complex, cross-functional initiatives
• Design and implement the partner integration AI/ML plane for embedded DFM AI + IQE integration with Teamcenter and Designcenter
• Build the real-time ML serving architecture and low-latency signal path to return DFM and pricing feedback in the designer’s environment
• Define input/output data contracts and implement MLOps, governance, and observability for mission-critical, public-marketplace partner integration
• Develop cloud production systems for real-time endpoints and MLOps, integrated with Xometry platform systems and infrastructure
• Tackle cross-domain technical problems by evaluating variable factors and aligning technical decisions to business and engineering objectives
• Identify opportunity areas early, take ownership of new processes and solutions, and build multi-quarter technical roadmaps
• Apply automated testing practices plus parallel and distributed computing approaches, including secure software development for ML systems
• Collaborate with engineers, product managers, data scientists, and business stakeholders to translate requirements into robust solutions
• Conduct and contribute to design reviews, code reviews, and technical mentorship to raise team capability
• Stay current with ML/AI advances and introduce relevant approaches, tools, and frameworks into production work

Requirements

• Bachelor’s degree in a STEM field (or equivalent experience) plus 6-8 years in machine learning engineering, with a proven record of owning and delivering complex production ML systems
• Strong expertise in ML/AI methods including Gradient Boosting and Deep Learning and/or Generative AI, with emphasis on backend scalability and reusable components
• Hands-on experience deploying real-time ML products at scale in cloud environments, with AWS strongly preferred (auto-scaling, monitoring, alerting)
• Advanced proficiency in Python and ML/AI frameworks such as TensorFlow and PyTorch (or similar)
• Solid software engineering fundamentals, including data structures and algorithms
• Demonstrated MLOps experience: model monitoring, data drift and concept drift detection, automated retraining, and redeployment pipelines
• CI/CD pipeline proficiency (e.g., GitHub Actions), test-driven development, and infrastructure as code (e.g., Terraform)
• Experience profiling and optimizing existing ML deployments for latency and throughput
• Ability to work independently on ambiguous assignments, determine methods and procedures, and communicate across engineering, product, and business audiences
• Experience with modern modeling techniques including Transformers, self-supervised pre-training, LLMs, and/or generative AI
• Knowledge of containers and orchestration (Kubernetes) plus cloud-native distributed systems
• Manufacturing, supply chain, or marketplace domain experience is a plus (curiosity and drive are emphasized)

Technologies

• Python
• TensorFlow
• PyTorch
• Gradient Boosting
• Deep Learning
• Generative AI frameworks
• AWS
• CI/CD pipelines
• GitHub Actions
• Test-driven development
• Terraform
• 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
• Infrastructure as code

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

Location: Denver, CO (hybrid)

Compensation: USD 200,000 - 220,000 per year