Staff Machine Learning Engineer

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. You will be responsible for designing the real-time ML serving architecture and the low-latency signal path that delivers DFM and pricing feedback directly into the designer's environment. This includes defining the data contracts for model inputs/outputs and implementing the MLOps, governance, and observability required for a mission-critical, public-marketplace partner integration.
• 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, evaluate variable factors, and deliver solutions that meet both business and technical objectives.
• Set the Standard – Proactively surface opportunity areas, take ownership of new processes and solutions, and develop multi-quarter roadmaps to accomplish key technical objectives.
• Champion quality and security – Apply best practices in automated testing, parallel and distributed computing, and secure software development across ML systems.
• Collaborate broadly – Partner with engineers, product managers, data scientists, and business stakeholders to translate requirements into robust technical solutions.
• Mentor and elevate – Guide other engineers through design reviews, code reviews, and technical mentorship, raising the overall capability of the team.
• Stay current – Keep pace with advances in ML/AI and bring relevant new approaches, tools, and frameworks into practice.

Qualifications

• Bachelor's degree in a STEM field (or equivalent experience) plus 6-8 years of experience in machine learning engineering, with a 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 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, PyTorch, or similar.
• 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 and 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.
• Able to operate 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 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 — but curiosity and drive matter more.