Staff AI Platform Engineer (ML Ops & Distributed Systems)

Relativity; Illinois; Hybrid; USD 205,000 - 307,000; Full time

Benefits offered by this job

DTO

Parental leave

Equity program

Competitive salary

Job summary

Relativity is hiring Staff AI Engineers to define the long-term ML Ops roadmap and guide production-grade machine learning platforms. You will lead training, inference, and monitoring architectures, while mentoring engineers across multiple teams and ensuring security, reliability, and cost efficiency.

You will partner with product and data science leaders to deliver cross-functional roadmaps, evaluate new ML Ops technologies, and champion best practices that scale with the company’s

Qualifications

• 8+ years of professional software engineering experience.
• 5+ years in ML/AI or big data environments.
• 4+ years of technical leadership across multiple teams.
• Expert-level Python/Java/Scala for production systems.
• Hands-on with Docker/Kubernetes and IaC tools (Terraform or Pulumi).
• Experience building and operating CI/CD pipelines for ML workflows (Prefect or Airflow).
• Experience deploying services on AWS, Azure, or GCP and strong security practices.

Responsibilities

• Define and evangelize the multi-year ML Ops roadmap aligned with product and research goals.
• Provide direction and mentorship across engineering teams and organizations.
• Design training, inference, and monitoring systems focused on scalability and reliability.
• Collaborate with product and data science leaders to scope cross-functional roadmaps.
• Stay current with ML Ops tech and establish engineering standards and best practices.
• Lead architecture reviews and mentor senior engineers across the org.

Skills

Python

Java

Scala

Docker

Kubernetes

CI/CD

ML Ops

Cloud platforms

Education

Master’s or PhD in CS/Engineering/Math

Tools

Terraform

Pulumi

Prefect

Airflow

Spark

Kafka

Job description

Relativity is hiring Staff AI Engineers to define the long-term ML Ops roadmap and guide production-grade machine learning platforms. You will lead training, inference, and monitoring architectures, while mentoring engineers across multiple teams and ensuring security, reliability, and cost efficiency.

You will partner with product and data science leaders to deliver cross-functional roadmaps, evaluate new ML Ops technologies, and champion best practices that scale with the company’s