Jobs › Relativity

Staff AI Engineer

Relativity · IL · Full time

Posted Sep 30, 2026 · We last checked this listing on Oct 6, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Tell me about an AI feature you took from prototype to production. What changed between the demo and the real thing?

What they are checking: Whether they understand the gap between a working demo and a reliable product customers trust.

As a staff engineer, how do you set technical direction across several teams building AI features?

What they are checking: Whether they can influence architecture across teams without direct authority.

Tell me about a time an AI system you built behaved badly in production. How did you catch it?

What they are checking: Whether they have monitoring in place and respond to failures with real fixes.

What would you do

A product manager wants to ship an AI feature that your evaluations say is not ready. What do you do?

What they are checking: Whether they can make a clear, data-backed case and offer options instead of a flat no.

How do you keep up with fast changes in AI models and tools without chasing every new thing?

What they are checking: Whether they adopt new technology with discipline, based on measured gains.

About the work

How would you evaluate a model that summarizes or classifies legal documents when lawyers need high accuracy?

What they are checking: Whether they build rigorous evaluations with expert-labeled data rather than relying on impressions.

Walk me through how you would design a retrieval system over millions of documents in a litigation matter.

What they are checking: Whether they understand chunking, embeddings, ranking and scale tradeoffs in real retrieval systems.

How do you reduce hallucinations when the output may be used in high-stakes legal work?

What they are checking: Whether they ground outputs in sources, cite evidence and design for verification by the user.

How do you balance model quality, latency and cost on a cloud platform serving many customers?

What they are checking: Whether they make practical engineering tradeoffs instead of always picking the biggest model.

How do you make sure customer data used with AI stays private and is not mixed across customers?

What they are checking: Whether they design for data isolation, which is essential when firms trust the platform with confidential matters.

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The full job description

As published by Relativity.

Posting Type Hybrid/Remote 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. Job Description and Requirements WHAT WE DO The AI team at Relativity is focused on helping users discover the truth faster and act on data with confidence through AI-powered capabilities embedded throughout the discovery process. • Build trusted AI solutions that improve user experiences, products, investigations, and legal matters. • Develop world-class tools to solve complex challenges through experimentation and innovation. • Leverage AI across all stages of the discovery lifecycle to improve outcomes and optimize product operations. • Invest in modern data infrastructure, data pipelines, and data lake technologies that enable large-scale AI and analytics. • Foster a culture of exploration, experimentation, continuous learning, and innovation. ABOUT THE ROLE Staff AI Engineers at Relativity operate as domain architects and strategic accelerators. This role works across multiple engineering and data science teams to define the long-term vision for Relativity’s machine learning platform, guide high-impact initiatives from concept through production, and ensure AI capabilities remain secure, reliable, scalable, and cost-effective. WHAT YOU’LL DO • Define and evangelize the multi-year technical roadmap for ML Ops, aligning platform architecture with product and research objectives. • Provide technical direction and mentorship across multiple engineering teams and organizations. • Drive the design and architecture of training, inference, and monitoring systems focused on scalability, extensibility, performance, and reliability. • Partner with product, program, and data science leaders to scope and deliver cross-functional roadmaps. • Evaluate emerging ML Ops technologies and establish engineering standards and best practices. • Champion advanced optimization techniques, including sparsity, quantization, and pruning, to improve performance and cost efficiency. • Collaborate with security teams to maintain strong data protection practices and responsible AI standards. • Lead architecture reviews, publish engineering best practices, and mentor senior and lead engineers across the organization. WHAT WE’RE LOOKING FOR Required • 8+ years of professional software engineering experience. • 5+ years of experience working in ML/AI or big data environments. • 4+ years of technical leadership experience across multiple teams. • Expert-level proficiency in Python, Java, or Scala for production systems. • Deep hands-on experience with Docker, Kubernetes/Helm, and infrastructure-as-code tools such as Terraform or Pulumi. • Proven experience building and operating CI/CD pipelines for machine learning workflows using technologies such as Prefect or Airflow. • Experience deploying and operating secure, monitored services on AWS, Azure, or GCP. • Demonstrated ability to mentor senior engineers, influence technical strategy, and drive cross-team initiatives to successful completion. Preferred • Master’s or PhD in Computer Science, Engineering, Mathematics, or a related field. • Recognized technical thought leadership through open-source contributions, conference presentations, or publications. • Experience scaling ML platforms utilizing distributed data technologies such as Spark or Kafka. • Expertise implementing advanced model optimization techniques, including compression, pruning, and quantization, in production 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. Relativity is committed to competitive, fair, and equitable compensation practices. This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives. The expected salary range for this role is between following values: $205,000 and $307,000 The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position. Required Skills: Algorithms, Artificial Intelligence (AI), Big Data, Cloud Computing, Data Science, Deep Learning, Distributed Systems, Natural Language, Python (Programming Language), Software Engineering

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