Staff Machine Learning Engineer

Overview You will lead the design and delivery of large-scale ML systems, architect cross-system integrations, and set engineering standards for production ML at Xometry. This senior IC role centers on the DFM AI + IQE integration with partner ecosystems, delivering real-time ML insights and pricing feedback to designers. You collaborate with data scientists, engineers, and product managers to ship impactful ML capabilities and raise the bar for AI across the platform. You will influence technical direction, governance, and observability in a high-impact, fast-moving environment. Compensation / Benefits 401(k) matchmedical, dental and vision insurancelife and disability insurancegenerous paid time offvacation, sick leave, holidaysmaternity and bonding leave Responsibilities Own end-to-end lifecycle from requirements to release for complex, cross-functional initiatives Architect and build the high-performance AI/ML plane for the DFM AI + IQE integration with Teamcenter and Designcenter Design real-time ML serving architectures and low-latency data paths for feedback into designers’ environments Define data contracts for model inputs/outputs and implement MLOps, governance, and observability for mission-critical integrations Develop cloud-based production systems powering real-time endpoints and MLOps Solve ambiguous, cross-domain technical problems to meet business and technical goals Set standards, drive multi-quarter roadmaps, and own process improvements Champion quality and security with automated testing, distributed computing, and secure software practices Collaborate with engineers, product managers, data scientists, and stakeholders to translate requirements into robust solutions Mentor peers through design/code reviews and technical guidance Key requirements 6-8 years in machine learning engineering with ownership of production ML systems Deep expertise in ML/AI including gradient boosting, deep learning, and generative AI, with a focus on backend scalability Hands-on experience with real-time ML in cloud environments (prefer AWS) including autoscaling, monitoring, and alerting Proficiency in Python and frameworks such as Tensor Flow or PyTorch Strong software fundamentals, data structures, and algorithmsMLOps: model monitoring, drift detection, automated retraining and redeploymentCI/CD pipelines and infrastructure as code (e.g., Git Hub Actions, Terraform)Latency/throughput optimization for ML deployments Ability to work independently on new/ambiguous work and communicate across audiences Experience with transformers, self-supervised pre-training, LLMs, or generative AIKnowledge of containers, Kubernetes, and cloud-native distributed systems Manufacturing, supply chain, or marketplace domain experience is a plusstrong collaboration across cross-functional teamsclear communication with technical and non-technical stakeholdersability to mentor and raise team capability Gradient Boosting methods Deep Learning Generative AI frameworks