About Xometry
Xometry (NASDAQ: XMTR) powers the industries of today and tomorrow by connecting the people with big ideas to the manufacturers who can bring them to life. Xometry’s digital marketplace gives manufacturers the critical resources they need to grow their business while also making it easy for buyers at Fortune 1000 companies to tap into global manufacturing capacity.
About the Role
Xometry is looking for a Staff Machine Learning Engineer to join our growing AI/ML team. This is a senior individual contributor role with broad technical scope and meaningful organizational impact. You will lead the design and delivery of complex ML systems, architect integrations across our tech stack, and set the engineering standard for how we build and deploy machine learning solutions at scale. You will work closely with data scientists, engineers, and product managers to bring high-impact ML capabilities into production. Everything you build will matter. A defining piece of this role is owning the AI/ML architecture behind one of Xometry's highest-leverage strategic initiatives: the DFM AI + IQE integration. You will be the data engineering lead for the digital thread that connects Xometry's platform to our partner’s ecosystem — Solid Edge, NX, Designcenter, and Teamcenter — building the pipelines, contracts, and observability that move quotes, parts, manufacturability signals, and pricing between the two systems in real time. The system you design is what takes the innovative digital thread operating at "science fiction speed" from ideation to reality.
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.
- Ability 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.