Staff AI Engineer

Relativity is building production-grade AI/ML capabilities to help identify and respond to data breaches at scale, while meeting regulatory deadlines. In this Staff AI Engineer role (Illinois, hybrid), you will architect, build, and operate AI/ML systems and the data pipelines that power them, strengthening reliability, security, and maintainability across the Data Breach Response (DBR) engineering organization.

Location: Illinois (hybrid)
Compensation: USD 205,000 - 307,000 per year
Experience: 10+ years

What you’ll do

• Architect end-to-end AI/ML solutions, applying principles across reliability, scalability, security, cost, and maintainability throughout the AI/ML lifecycle.
• Build out the AI Engineering discipline within DBR and extend practices across the broader engineering organization.
• Collaborate across Relativity to raise AI Engineering standards and elevate how teams design and deliver AI systems.
• Partner with product and engineering leadership to define the AI strategy for DBR, then lead execution of that strategy across DBR engineering teams.
• Mentor junior AI Engineers, as well as senior and lead engineers.
• Build, deploy, monitor, and operate AI/ML systems in production, including pipelines and evaluation harnesses used by Applied Science.
• Design and operate data pipelines and platforms that feed AI/ML systems, working with Product, Applied Science, and DevOps.
• Determine how data, models, services, and deployment patterns fit together, balancing latency, throughput, accuracy, and regulatory constraints.
• Prevent issues through system design and automation by building alerting, conducting root cause analysis, and sharing learnings across teams.
• Communicate risks and technical decisions with stakeholders across the organization.

What you bring

• 10+ years of experience designing, building, and operating cloud-native distributed systems, including 5+ years focused on AI/ML systems and data platforms in production at scale.
• Technical leadership across multiple teams or domains, with experience setting direction and aligning teams on architecture decisions.
• Background in AI/ML architecture, data engineering, and security at scale.
• Fluency with ML frameworks and tooling for deploying, monitoring, and operating models, plus the infrastructure that supports evaluation by Applied Science.
• Experience translating business goals into technical strategy.
• Experience spanning AI/ML Engineering, Data Engineering, MLOps, Distributed Systems, Cloud Architecture, Technical Leadership, and Responsible AI or Governance.
• Experience shaping data platforms, evaluation infrastructure, or AI governance standards.
• Track record of driving organization-wide AI/ML engineering improvements in regulated or large-scale environments.

Tools and focus areas

• AI/ML, MLOps, Distributed Systems, Cloud Architecture
• Python (Programming Language)

Compensation and benefits

• Competitive salary, benefits, DTO, parental leave, and an equity program.
• Annual performance bonus.
• Long-term incentives.