About the Role
Xometry is looking for a Machine Learning Engineer II who is excited about advancing machine learning capabilities and bringing models into production at scale. In this role, you’ll design, deploy, and maintain robust statistical and machine learning models, working closely with data scientists to translate research and experimentation into reliable, high-impact systems. You’ll apply strong data intuition and engineering judgment to improve model performance, reliability, and observability, while building predictive models that support pricing, cost estimation, and sourcing recommendations.
What You'll Do:
- 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.
Qualifications:
- A bachelor’s degree is required, but an advanced degree (M.S. or PhD) in computer science, machine learning, AI, or a related field is highly preferred.
- Experience deploying and maintaining machine learning models in production environments.
- 4+ years of experience in machine learning, focusing on data engineering and/or data science.
- Proficient in Python, including key libraries such as PyTorch, TensorFlow, pandas, and numpy.
- Strong background in probability, statistics, and optimization techniques relevant to generative modeling.
- Familiarity with cloud computing resources and tools for model training and deployment (e.g., AWS SageMaker).
- Familiar with software engineering principles, including version control, reproducibility, and continuous integration.
- Experience in the manufacturing, supply chain, or similar industries is a plus.