The ad says“Experience with RAG, agentic systems, and/or LLM/LMM optimization”
The ad says“B2B SaaS background, especially in products with significant AI or data components”
The ad says“Experience with RAG, agentic systems, and/or LLM/LMM optimization”
The ad says“If you’ve built ML/LLM-powered data pipelines, love the challenge of turning unstructured input into structured, reliable output, and want broad impact across a fast-growing product org, this role is for you”
The ad says“5+ years of product management experience, including time at a high-growth startup (Series B–E)”
The ad says“Experience with RAG, agentic systems, and/or LLM/LMM optimization”
The ad says“Technical fluency: you’ve worked closely with ML engineers, backend engineers, or data teams and can hold your own in technical discussions about model performance, data pipelines, and system design”
We’re looking for a Product Manager to join our AI Platform group and own the systems that turn messy, unstructured case materials into accurate, structured “source of truth” data
This isn’t your typical PM role: it’s highly technical, backend-heavy, and focused on scalable AI extraction and data infrastructure
You’ll work closely with senior ML and backend engineers to build the models, evals, pipelines, tooling, and APIs that power all of EvenUp’s products
You’ll be responsible for a mission-critical part of our AI strategy: making sure the entities we generate are accurate, consistent, and trustworthy enough for law firms to rely on
Own the roadmap for a key part of our AI Platform, potentially including RAG services, text and image extraction and analysis services, model evaluation and deployment tools, and more
Partner with ML and backend engineers to build scalable, reliable, and cost-effective systems that support product teams across the company
Define success metrics, analyze performance and cost data , and use insights to inform priorities
Work closely with other product managers teams to understand end-user needs and develop reusable platform capabilities that accelerate AI development
Drive strategic decisions around model performance, vendor selection, and infrastructure scaling in a fast-evolving ecosystem
Define success metrics for entity quality and pipeline performance
Partner with other product managers and downstream teams to understand where entity quality or coverage gaps are limiting product experiences, and translate that into a prioritized platform roadmap
Drive decisions on model performance, annotation strategy, and integration with continuous inference systems as the underlying ML approaches evolve
Benefits
Flexible working hours to match your style
Offsites - get to meet your coworkers on a fully-expenses trip ever 6-12 months
A variety of virtual team events such as game nights & happy hours
Choice of medical, dental, and vision insurance plans for you and your family
Flexible paid time off and 10+ holidays per year
A stipend to upgrade your home office for fully-remote roles
401k for US-based employees
Preferred experience
If you’ve built ML/LLM-powered data pipelines, love the challenge of turning unstructured input into structured, reliable output, and want broad impact across a fast-growing product org, this role is for you
Experience building LLM/LMM-powered products or data infrastructure, with a strong understanding of how modern AI extraction and structured-data systems work end-to-end
5+ years of product management experience, including time at a high-growth startup (Series B–E)
Technical fluency: you’ve worked closely with ML engineers, backend engineers, or data teams and can hold your own in technical discussions about model performance, data pipelines, and system design
Comfort operating in a backend-heavy environment where your “users” are often internal teams and downstream product surfaces, and your work directly impacts data accuracy, reliability, and trust
Analytical mindset: you use data to make decisions and are comfortable managing quality metrics (precision/recall, entity accuracy, coverage) alongside operational ones (latency, throughput)
Experience with RAG, agentic systems, and/or LLM/LMM optimization
Familiarity with building internal platforms or tools that support other product teams
B2B SaaS background, especially in products with significant AI or data components
