About this role
Bridging the gap between complex digital marketplaces and global manufacturing requires sophisticated machine learning systems that can process pricing and manufacturability signals in real time. Xometry needs a senior technical leader to architect the integration connecting their platform to partner ecosystems like Solid Edge and Teamcenter, turning high-leverage strategic initiatives into functional production systems.
Operating as a senior individual contributor within the AI and machine learning team, the incoming engineer will guide the complete lifecycle of ML capabilities from initial requirements to scalable release. Responsibilities include designing low-latency signal paths and establishing MLOps standards alongside product managers and data scientists.
Ideal candidates are experienced practitioners who thrive on ambiguous technical challenges and possess a strong background in deploying cloud-based ML products. A collaborative mindset and a commitment to technical excellence are essential for success in this role.
What you will do
- Lead the end-to-end lifecycle of complex machine learning initiatives from requirements gathering through release.
- Architect and build the high-performance AI and ML layer for embedded partner integrations.
- Develop cloud-based production systems that power real-time endpoints and integrated MLOps infrastructure.
- Navigate complex cross-domain technical challenges to deliver solutions meeting business and technical objectives.
- Apply best practices in automated testing, distributed computing, and secure software development across ML systems.
- Partner with cross-functional stakeholders including engineers, product managers, and data scientists to build robust solutions.
- Mentor other engineers through design reviews, code reviews, and technical guidance.
What you need
- Hold a bachelor's degree in a STEM field or possess equivalent practical experience.
- Bring 6 to 8 years of machine learning engineering experience with a track record of delivering production systems.
- Demonstrate deep expertise in gradient boosting methods, deep learning, or generative AI frameworks with a focus on backend scalability.
- Possess hands-on experience deploying real-time ML products at scale in cloud environments, with a strong preference for AWS.
- Exhibit strong proficiency in Python alongside advanced frameworks such as TensorFlow or PyTorch.
- Maintain solid grounding in software engineering fundamentals, data structures, algorithms, and MLOps practices.
- Show proficiency with CI/CD pipelines, test-driven development, and infrastructure as code tools like Terraform.
Nice to have
- Experience with state-of-the-art modeling techniques including transformers, self-supervised pre-training, large language models, or generative AI
- Knowledge of containers, container orchestration with Kubernetes, and cloud-native distributed systems
- Background in manufacturing, supply chain, or marketplace environments
Benefits
- 401(k) match
- Medical, dental, and vision insurance
- Life and disability insurance
- Generous paid time off including vacation, sick leave, floating and fixed holidays, maternity and bonding leave
- EAP and other wellbeing resources
Skills and tech stack
Questions candidates ask
What is the primary focus of this role at Xometry?
The role centers on owning the AI and machine learning architecture behind the DFM AI and IQE integration, serving as the data engineering lead for the digital thread connecting Xometry's platform to partner ecosystems.
What cloud environment does the team prefer?
Amazon Web Services is strongly preferred for deploying real-time ML products at scale.
What level of experience is required?
Applicants need 6 to 8 years of experience in machine learning engineering with a track record of owning and delivering complex ML systems in production.
What benefits are included with the position?
The company offers a competitive benefits package featuring a 401(k) match, medical, dental, and vision insurance, life and disability insurance, generous paid time off, and employee assistance resources.