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Company

Xometry · 14 hours ago

Principal ML Engineer, ML Platform Engineering

United States (Remote)

Full-time

Remote

Senior Level

7+ years exp

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3D PrintingArtificial Intelligence (AI)

No H1B

U.S. Citizen Only

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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.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Machine Learning EngineeringCloud Infrastructure (AWS)Machine Learning LifecycleSoftware Engineering PrinciplesMachine Learning TechniquesInfrastructure as Code (IaC)REST API DesignPython ProgrammingAWS MicroservicesContainerization DockerContainerization KubernetesMultimodal Data ProcessingManufacturing Industry ExperienceTechnical Communication

Required

At least 7 years of experience in machine learning engineering, software engineering, data science, or similar technical role.

A bachelor’s degree is required.

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 in software engineering principles, including parallel and distributed computing, version control, reproducibility, and continuous integration.

Strong technical expertise in machine learning techniques and algorithms, with emphasis on their impact to infrastructure implementation.

Strong technical expertise in large-scale language and vision models (Transformers, GPT, VLMs, LLMs), deep learning (PyTorch, Tensorflow).

Strong technical expertise in Infrastructure as Code (IaC), especially Terraform.

Strong technical expertise in REST API design and implementation.

Strong technical expertise in object oriented and functional programming in Python.

Strong technical expertise in 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.

Experience with 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.

Must be a US Citizen or Green Card holder (ITAR).

Preferred

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.

Experience in the manufacturing, supply chain, or similar industries is a plus.