Staff AI Engineer

Description

The company builds legal data intelligence software, including an AI-powered cloud platform used to sort large volumes of complex information for litigation, investigations, regulatory inquiries and data breach responses.

The AI engineering team builds and runs the AI and ML systems behind privacy programs, confidential information detection and misconduct detection, at production scale and under real legal stakes.

The role sits in Data Breach Response engineering, made up of four teams spread across North America and Poland. The DBR product uses AI to find and respond to data breaches at scale, often against strict regulatory deadlines and tight timelines.

Responsibilities

• Design end-to-end AI and ML solutions that are reliable, scalable, secure, cost-effective and maintainable across the full model lifecycle.
• Help establish AI engineering as a discipline within DBR and the wider engineering organization.
• Raise AI engineering practices and standards across the company.
• Mentor junior AI engineers and senior and lead engineers.
• Work with product and engineering leadership to set the AI strategy for DBR and lead its delivery across the DBR teams.
• Build, deploy, monitor and run AI and ML systems in production, including the pipelines and evaluation harnesses Applied Science uses.
• Build and run the data pipelines and platforms that feed these systems, working with Product, Applied Science and DevOps.
• Decide how data, models, services and deployment patterns fit together, weighing latency, throughput, accuracy and regulatory constraints.
• Prevent problems through system design and automation, set up alerting, run root cause analysis and share what you learn.
• Explain risks and technical decisions to stakeholders.

Requirements

• 10+ years designing, building and running cloud-native distributed systems, with 5+ years on AI and ML systems and data platforms in production at scale.
• Technical leadership across several teams or domains, including setting direction and getting teams aligned on architecture decisions.
• Background in AI and ML architecture, data engineering and security at scale.
• Fluency with the ML frameworks and tooling used to deploy, monitor and run models, and to build the infrastructure Applied Science uses to evaluate them.
• Experience turning business goals into technical strategy.
• Experience in AI/ML engineering, data engineering, MLOps, distributed systems, cloud architecture, technical leadership, and responsible AI or governance.

Conditions

The role is eligible for total compensation: a competitive base salary, an annual performance bonus and long-term incentives.

The expected base salary range is $205,000 to $307,000. Final pay depends on experience, skills, qualifications and internal pay equity, and the company does not typically offer the top of the range so there is room for future salary growth.

The company is committed to fair and equitable pay.

The company is an equal opportunity employer.

Benefits

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