Principal ML Engineer, ML Platform Engineering

We are looking for a principal machine learning engineer to join our core machine learning platform engineering team. In this role, you will partner closely with the AI/MLE leadership team to deliver the vision and technical implementation for the foundational infrastructure leveraged by Xometry’s AI/ML solutions, including the Instant Quoting Engine® and other AI/ML products powering the Xometry marketplace.

This will be a high visibility role working hands-on to deliver a core aspect of the Xometry ecosystem. You will be given the opportunity to continually challenge yourself, drive innovation, have ownership of your work, and play a crucial role in the Xometry platform. 

Responsibilities:

• Hands-On Technical Leadership: Adopt a 'lead by example' approach by actively coding and troubleshooting, as well as creating documentation and technical diagrams.
• Teaching & Mentorship: You will serve as a mentor and guide to engineers across the organization, teaching and mentoring them to grow their skills.
• Code Review: You will do code review and mentor others within the organization regarding best practices in ML Engineering.
• Operational Excellence: Guarantee the delivery of superior infrastructure and software that not only meets but exceeds customer expectations, while aligning with the strategic business timelines.
• Collaborative Strategy: Forge strong partnerships with product managers, data scientists, and company leadership to promote a culture of open communication and integrated team dynamics.
• Guide Innovation: Champion the adoption of cutting-edge technologies, methodologies, and practices to enhance problem-solving efficiency and effectiveness across the AI/ML organization.

Qualifications:

• At least 7 years of experience in machine learning engineering, software engineering, data science, or similar technical role.
• 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 preferred and may substitute for some years of experience.
• Demonstrated experience designing and deploying cloud infrastructure (AWS preferred) to support machine learning, and machine learning models, with considerations for scale, reliability and security.
• Deep understanding of the machine learning lifecycle and related infrastructure needs - feature stores, a/b testing, model registration, drift detection, automated retraining, etc.
• Strong technical expertise. You will need to either have or demonstrate the ability ability to quickly build technical expertise in the following:
• Software engineering principles, including parallel and distributed computing, version control, reproducibility, and continuous integration.
• Machine learning techniques and algorithms, with emphasis on their impact to infrastructure implementation
• Including large-scale language and vision models (Transformers, GPT, VLMs, LLMs), deep learning (PyTorch, Tensorflow) 
• Infrastructure as Code (IaC), especially Terraform
• REST API design and implementation
• Object oriented and functional programming in Python
• Multimodal data processing (e.g., combining text, image, and 3D data).
• Experience with AWS microservices including SageMaker, Service Catalog, IAM, Lambda, Cloudwatch, ECR, EKS, and Kinesis
• Containerization technologies (Docker and Kubernetes)
• Demonstrated ability to interact and communicate effectively at all levels of the organization, from executives to product managers and a wide variety of stakeholders and contributors
• Experience in the manufacturing, supply chain, or similar industries is a plus.
• Must be a US Citizen or Green Card holder (ITAR)

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