Job description
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.
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.
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.
- 4+ years of experience in machine learning, focusing on data engineering and/or data science.
- Expertise in large-scale language and vision models (e.g., Transformers, GPT, VLMs).
- 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.
Additional Qualifications
Experience with multimodal data processing (e.g., combining text, image, and 3D data).
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Xometry is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status.
For US based roles: Xometry participates in E-Verify and after a job offer is accepted, will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.
Xometry is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status.
For US based roles: Xometry participates in E-Verify and after a job offer is accepted, will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.
