Staff AI Engineer

Posting Type

Remote/Hybrid

Job Overview

WHO WE ARE

Relativity is a leading legal data intelligence company building technology that helps users organize data, discover the truth, and act on it with confidence. Our AI-powered, cloud platform, RelativityOne, transforms massive volumes of complex information into actionable insights for litigation, investigations, regulatory inquiries, data breach responses, and other high-stakes legal work where accuracy and trust are crucial.

The world’s largest law firms, corporations, and government agencies rely on Relativity’s legal AI software to securely surface and manage the most relevant and impactful information in their matters. Beyond our commercial impact, we are committed to expanding access to technology and supporting pro bono legal work.

WHAT WE DO

AI engineers at Relativity build and operate the AI/ML systems behind privacy programs, confidential information detection, and misconduct detection at production scale and under real legal stakes. The work spans building, deploying, and operating AI/ML systems, the data platforms that feed them, and the architecture that ties them together.

Job Description and Requirements

ABOUT THE ROLE 

This role sits within Data Breach Response (DBR) engineering, which comprises four teams distributed across North America and Poland. DBR is an AI-powered product that helps identify and respond to data breaches at scale, against strict regulatory deadlines and tight timelines. 

WHAT YOU’LL DO 

• Architect end-to-end AI/ML solutions, applying reliability, scalability, security, cost, and maintainability principles across the AI/ML lifecycle. 

• Build the AI Engineering discipline in DBR and the broader engineering organization. 

• Work across Relativity to elevate AI Engineering practices and standards. 

• Mentor junior AI Engineers, as well as senior and lead engineers. 

• Partner with product and engineering leadership to define the AI strategy for DBR. 

• Lead execution of the AI strategy across DBR engineering teams. 

• Build, deploy, monitor, and operate AI/ML systems in production, including pipelines and evaluation harnesses used by Applied Science. 

• Build and operate the data pipelines and platforms that feed AI/ML systems, partnering with Product, Applied Science, and DevOps. 

• Determine how data, models, services, and deployment patterns fit together, balancing latency, throughput, accuracy, and regulatory constraints. 

• Prevent problems through system design and automation, build alerting, conduct root cause analysis, and share learnings. 

• Communicate risks and technical decisions across stakeholders. 

WHAT WE’RE LOOKING FOR 

Required 

• 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 used to deploy, monitor, and operate models and build the infrastructure against which Applied Science evaluates them. 

• Experience translating business goals into technical strategy. 

• Experience with AI/ML Engineering, Data Engineering, MLOps, Distributed Systems, Cloud Architecture, Technical Leadership, and Responsible AI or Governance. 

Preferred 

• 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. 

WHY WE COULD BE A GREAT FIT 

Impactful Mission 

• Build systems that help customers organize data, discover the truth, and act on it in high-stakes legal matters. 

Engineering at Scale 

• Work on distributed, cloud-native systems that process large volumes of data. 

Cutting-Edge Technology 

• Build with AI, cloud platforms, and scalable architectures shaping legal tech. 

Growth and Ownership 

• Gain experience owning systems end-to-end across cloud and distributed environments. 

Collaborative Culture 

• Work in a team focused on knowledge sharing and continuous improvement. 

Inclusive Environment 

• Diverse perspectives create stronger teams and better outcomes. 

Compensation and Benefits 

• Competitive salary, benefits, DTO, parental leave, and equity program. 

Algorithms, Artificial Intelligence (AI), Big Data, Cloud Computing, Data Science, Deep Learning, Distributed Systems, Natural Language, Python (Programming Language), Software Engineering