Senior ML Engineer — Real-Time AI Platform Lead

Xometry; Denver (CO); Hybrid; USD 200,000 - 220,000; Full time

Benefits offered by this job

401(k) match

Medical, dental, vision insurance

Paid time off including vacation/sick/

Floating holidays

Job summary

Xometry is seeking a Staff Machine Learning Engineer to lead the design and delivery of complex ML systems and architect integrations across our tech stack. You will own the AI/ML architecture behind high-leverage initiatives like the DFM AI + IQE integration and serve as the data engineering lead for the digital thread connecting Xometry's platform to partner ecosystems.

You will collaborate with data scientists, engineers, and product managers to deploy high-impact ML capabilities at scale,

Qualifications

• Bachelor's degree in a STEM field (or equivalent experience).
• 6-8 years of experience in machine learning engineering with production ML systems.
• Experience deploying real-time ML products at scale in cloud environments (AWS preferred).

Responsibilities

• Lead end-to-end lifecycle from requirements gathering through release.
• Architect and build the high-performance AI/ML layer for DFM AI + IQE integration.
• Develop cloud-based production systems powering real-time endpoints and MLOps.
• Collaborate with engineers, data scientists, product managers, and stakeholders.
• Mentor other engineers and promote best practices across teams.

Skills

ML engineering

Python

TensorFlow

PyTorch

MLOps

CI/CD

GitHub Actions

Kubernetes

AWS

Transformers/LLMs

Education

Bachelor's degree in STEM

Tools

Docker

Terraform

Job description

Xometry is seeking a Staff Machine Learning Engineer to lead the design and delivery of complex ML systems and architect integrations across our tech stack. You will own the AI/ML architecture behind high-leverage initiatives like the DFM AI + IQE integration and serve as the data engineering lead for the digital thread connecting Xometry's platform to partner ecosystems.

You will collaborate with data scientists, engineers, and product managers to deploy high-impact ML capabilities at scale,