Backend / MLOps

Xometry (NASDAQ: XMTR) powers the industries of today and tomorrow by connecting the people with big ideas to the manufacturers who can bring them to life. Xometry’s digital marketplace gives manufacturers the critical resources they need to grow their business while also making it easy for buyers at Fortune 1000 companies to tap into global manufacturing capacity.

We are seeking a Senior Backend Software Engineer focused on Machine Learning Platforms to join our core ML Platform Engineering team. 

Your primary responsibility will be to design, build, and operate scalable backend systems and cloud infrastructure that enable rapid deployment, experimentation, monitoring, and operation of ML and Generative AI products. You will work closely with data scientists and applied ML engineers, who own model development, while you own the systems that make those models production-ready.

This is a 1-year contract

Technical Stack

• Python (backend services, ML tooling, automation)

• SQL (analytics, data access, debugging

Backend & APIs

• FastAPI / Flask (production ML & GenAI APIs)

• Asynchronous processing (Celery, background workers, event-driven patterns)

• RESTful and service-oriented architectures
  

What you will do

• ML Platform & Backend Engineering

  • Design and implement backend services, APIs, and workflows that support ML training, batch inference, and real-time inference.

  • Build production-grade ML infrastructure for deployment, scaling, monitoring, and experimentation of ML and GenAI systems.

  • Develop Python-based services and frameworks that abstract infrastructure complexity for data scientists.

• Cloud Infrastructure & Operations

  • Design, deploy, and operate cloud-native architectures on AWS, using Infrastructure as Code (Terraform).

  • Own services running in Kubernetes environments, including deployment, scaling, reliability, and on-call responsibilities.

  • Implement observability, monitoring, and alerting for ML services and data pipelines.
    

• MLOps & CI/CD

  • Build and maintain CI/CD pipelines (e.g., GitHub Actions) for ML services and infrastructure.

  • Support the full ML lifecycle: experimentation, model versioning, deployment, rollback, and monitoring.

  • Enable repeatable, secure, and compliant ML deployments (e.g., SageMaker endpoints, batch jobs, async pipelines).
    

• Collaboration & Ownership

  • Partner with data scientists and ML engineers to translate modeling needs into robust, scalable systems.

  • Participate actively in architecture discussions, code reviews, and technical design reviews.

  • Own the long-term health, reliability, and evolution of the services built by your team.
    

Required Qualifications

• 5+ years of experience as a backend software engineer, platform engineer, or MLOps engineer in fast-paced environments.

• Strong experience building backend systems and APIs in Python for production workloads.

• Hands-on experience designing and deploying AWS cloud infrastructure using Terraform (IaC).

• Solid understanding of distributed systems fundamentals: scalability, security, async processing, transactions, and failure modes.

• Strong communication skills and ability to collaborate with both technical and non-technical stakeholders.
  
  

Preferred Qualifications

• Proven experience building ML platforms, MLOps systems, or infrastructure supporting ML / GenAI products.

• Experience with CI/CD pipelines, particularly GitHub Actions.

• Familiarity with ML lifecycle tooling (e.g., model registries, experiment tracking, feature stores).

• Experience deploying real-time and batch ML inference services.

• Strong understanding of service-oriented architectures and API-driven system design.

• Experience with relational and/or NoSQL databases, including schema design, transactions, replication, and scaling strategies.

• Experience exposing ML capabilities via internal or external APIs used by multiple teams.