In this role, you will:

• Lead product development teams that build AI platform solutions to power RelativityOne’s AI feature set, including aiR for Review, while adhering to Relativity’s AI Principles  
• Evangelize AI platform infrastructure and tooling to drive adoption of generative AI and LLM capabilities (e.g.leveraging GPT-4o) that deliver on product needs and operate at massive scale 
• Oversee AI service procurement with a strategic approach that ensures capacity planning, cost-effectiveness, and the enhancement of broader product capabilities, ultimately driving world-class customer experiences. 
• Build user empathy and a deep understanding of user needs amongst your team and the broader business 
• The ideal candidate will have substantive AI experience, especially in the AI model lifecycle. They will bias towards data and have a strong proficiency in using data to monitor product health, set ambitious goals, and track success. They will passionately advocate for the benefits of our AI platform products and give confidence to the business and our customers that the world’s most powerful AI tooling for Legal Data Intelligence is underpinned by a reliable, highly-efficient, and principled foundation. 

Customer Centric:

• Connect frequentlywith internal customers in Data Science and AI application teams to gain a deep understanding of their day-to-day needs and business goals, in addition to validating solutions with them for feedback in product discovery. 
• Ensure we are solving business problems with the proper balance between delivering on immediate customer needs while also building capabilities that will deliver mid/long-term success. 
• Evangelize to internal AI application teams about the benefits of adopting AI platform components and tooling – be an expert at explaining the “why”. 
• Partner with Engineering in product discovery to build deep alignment on the problems to solve and key results we should target that define success. 
• Act as AI platform SME when external customers have detailed questions about our approach to building and supporting AI products in RelativityOne. 

Product Leadership:

• Own the product roadmap and backlog for your products, with a focus on outcomes we want to achieve over features we want to ship. Solicit feedback from internal SMEs, Engineering, Data Science, Product Managers, and others to drive confidence in decision-making. 
• Own the strategy and vision for your products, ensuring that we are maturing quarterly in each aspect of the ML Ops lifecycle to drive Data Science experimentation and model delivery at scale. 
• Advocate for Relativity’s AI Principles throughout internal decision-making, even when it’s inconvenient or slows us down. 
• Partner with AI service providers like Microsoft to evaluate AI offerings and lead planning on procurement. 
• Possess and apply an in-depth understanding of our AI product suite, giving confidence to teams that our supporting AI technology is informed by the needs of our end users and the strategy of our AI application teams. 
• Set, track, and report on key product metrics that are relevant to your domain and lead decision-making on expected outcomes for product work.   

Technical Leadership

• Work closely with your Engineering counterparts to ensure that the team delivers a product that meets our scalability, reliability, usability, observability, efficiency and performance standards.  
• Be well versed in technical aspects of ML Operations and broader AI tooling such that you can clearly communicate with development team members and make informed decisions about your product. We run an Azure stack with Prefect, Spark, and Databricks but welcome experience with alternative tooling. 
• Possess the technical acumen needed to understand the challenges faced by our internal customers, including the root cause of production incidents such that you can propose effective solutions for them alongside Engineering. 
• Cultivate a deep understanding of generative AI technology and large language models with an ability to lead technical conversations about Relativity’s use, implementation, decision-making process, and more.

Product Implementation

• Break down large projects into iterative releases. Create and share product implementation plans with measurable results for each milestone.  
• Anticipate the need to create alignment with cross-functional teams to solve problems together.   
• Help the team to translate validated designs and analysis into story cards with acceptance criteria.  

Preferred qualifications

• Professional experience with ML Ops (including working throughout the model lifecycle), AI tooling, generative AI, and LLMs 
• Very strong proficiency with data, including ability to use data to analyze and monitor product health, set goals, back up assumptions, and track success.  
• History of working with OKRs (or other outcome-based frameworks) that prioritizes outcomes over feature lists 

Minimum qualifications

• 5+ years of experience in software product management with 3+ years in AI specifically 
• Formal / collegiate education is not required to be considered for this role 

Compensation:

• 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 target of 10%, and long-term incentives. The expected salary range for this role is between $140,000 and $210,000. The final salary offered 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.