Staff AI Engineer
About The Position
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. 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. 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.
Requirements
- 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.
Nice To Haves
- 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.
Responsibilities
- 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.
Benefits
- Competitive salary, benefits, DTO, parental leave, and equity program.
- Competitive base salary, an annual performance bonus, and long-term incentives.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
