Principal ML Engineer - ML Platform Engineering

2 days ago

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Xometry

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Direct Metal Laser Sintering (DMLS) • CNC Machining • Production Manufacturing • Rapid Prototyping • 3D Printing

1001 - 5000

💰 $75M Series E on 2020-09

Description

• 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. • 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.

Requirements

• 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 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).