• Design and implement machine learning systems to predict future ad inventory,demand, and performance • Research and apply best practices for driving automation with respect to human review processes • Partner with multiple teams to shape and enhance shared systems and pipelines • Come up with creative ways to apply AI tools to develop innovative solutions • Collaborate with and lead backend engineers, data scientists, data engineers, and product managers to establish baselines, inform product decisions, and develop new technologies

• Own the end-to-end machine learning platform, including model development workflows, training, deployment, monitoring, retraining, and lifecycle management. • Define and execute the roadmap for scalable ML infrastructure supporting both real-time and batch use cases. • Lead applied machine learning initiatives supporting compliance and customer success, including areas such as: • Compliance monitoring, alerting, and investigation support • Customer success automation, prioritization, and insight generation • Internal operational tooling and responsible AI adoption • Partner with cross-functional stakeholders to translate complex business, customer, and regulatory problems into production ML solutions. • Build, mentor, and scale a small team of ML engineers and applied scientists, operating as a player-coach when needed.

• Take technical ownership of core components of recommendation and personalization systems (retrieval, ranking, evaluation). • Design and evolve two-tower / embedding-based retrieval models and downstream rankers. • Drive architectural and modeling decisions with a strong understanding of trade-offs between model quality, system complexity, latency, and cost. • Define and promote best practices for ML system design, experimentation, evaluation, and deployment. • Review ML designs, pipelines, and code with a focus on correctness, maintainability, and production readiness. • Act as a technical point of reference for ML-related decisions within the team. • Develop, train, and improve ML models for retrieval and ranking use cases. • Work with embedding-based deep learning models and classical ML approaches. • Perform in-depth data analysis, feature exploration, and systematic error analysis. • Build reproducible experiments and robust offline evaluation pipelines. • Optimize models for both offline metrics and online business KPIs. • Design and operate batch and real-time training and inference workflows in a cloud environment, with awareness of scalability and cost trade-offs. • Design, run, and analyze offline experiments and online A/B tests. • Own ML components in production, with a strong focus on reliability, observability, and safe iteration. • Monitor model performance and data quality in production. • Collaborate on scalable training and serving infrastructure for ML systems. • Participate in incident analysis related to ML systems and contribute to root-cause analysis and long-term fixes. • Design ML systems with failure modes in mind, including fallbacks and graceful degradation. • Work closely with Data Engineering on data pipelines and feature generation. • Partner with Product and Analytics to translate business goals into clear ML objectives and success metrics. • Act as a technical mentor for ML engineers, providing guidance on modeling, experimentation, and production ML. • Provide constructive feedback through code reviews and design discussions, supporting the growth of the team.

• Design, build, and optimize machine learning models to enhance Xometry’s platform and business operations. • Analyze large datasets to extract meaningful patterns and insights. • Collaborate with cross-functional teams to integrate machine learning models into production systems. • Learn and apply best practices in model evaluation, performance tuning, and deployment. • Influence technical direction by identifying opportunities to improve modeling approaches, data quality, and system architecture. • Work across teams to ensure machine learning solutions are explainable, maintainable, and aligned with business goals. • Help bridge the gap between research and production, ensuring models perform just as well in the real world as they do in notebooks. • Gain exposure to cutting-edge machine learning frameworks, tools, and techniques used in the manufacturing industry.