1. What is a Product Manager?

At EvenUp, the Product Manager role is a pivotal leadership position designed to bridge the gap between complex legal workflows and cutting-edge artificial intelligence. You are not just managing features; you are driving the mission to close the justice gap. Your work directly empowers personal injury lawyers to secure faster settlements and better outcomes for victims who have suffered due to accidents or negligence.

In this role, you act as the strategic hub for a vertical SaaS product that is transforming a legacy industry. You will lead the development of AI-powered solutions—such as automated document generation and negotiation support tools—that allow law firms to operate with unprecedented efficiency. You are responsible for translating high-level company strategy into actionable tactics, collaborating deeply with machine learning researchers, data scientists, and engineers to deliver value that is both technically sophisticated and intuitively usable for legal professionals.

This position offers a unique blend of B2B workflow optimization and AI innovation. You will be working in a high-growth environment backed by top-tier venture capital, where your decisions have a tangible impact on the business trajectory. Whether you are refining user interfaces with designers or diving into SQL to uncover usage patterns, your goal is to build products that are broadly and deeply adopted by a demanding user base.

2. Common Interview Questions

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

3. Getting Ready for Your Interviews

Preparation for the EvenUp Product Manager interview requires a shift in mindset. You need to demonstrate not only standard product management competencies but also a specific aptitude for data-driven decision-making and an ability to navigate the nuances of the legal tech space.

Focus your preparation on these key evaluation criteria:

User-Centric Problem Solving – You must demonstrate strong qualitative research skills. Interviewers want to see how you uncover deep customer pain points in complex workflows and translate them into product requirements. You should be able to discuss how you validate needs before building.

Data Fluency and Technical Aptitude – Unlike generalist PM roles, EvenUp places a heavy emphasis on quantitative skills. You will likely be evaluated on your ability to use data to tell a story. Familiarity with SQL and an ability to discuss AI/ML concepts intelligibly are critical differentiators here.

Execution and Velocity – As a fast-growing startup, the company values a "growth-focused" mindset. You need to show that you can prioritize ruthlessly, experiment quickly, and iterate based on feedback. Be prepared to discuss how you manage cross-functional sprints to ship high-quality features with high velocity.

Domain Adaptability – While you may not need to be a lawyer, you must show an ability to quickly grasp the "Personal Injury" domain. You will be evaluated on your capacity to build tools for sophisticated external business users, such as document generation or decision-support systems.

4. Interview Process Overview

The interview process for the Product Manager role at EvenUp is structured to assess both your strategic thinking and your tactical execution. Generally, the process moves quickly, often concluding within two to three weeks. It typically begins with a recruiter screen to align on background and logistics, followed by a screening call with a Hiring Manager. This initial conversation focuses heavily on your past experience with product management and your familiarity with AI technologies.

If you advance, you will move into a series of functional interviews. Candidates have reported speaking with cross-functional partners, including Engineering Managers, Data Engineers, and UI Engineers. This reflects the highly collaborative nature of the role. You should expect a mix of behavioral questions and specific competency checks. Notably, some candidates have encountered "quiz-like" questions regarding technical concepts or product definitions, so do not be surprised if the format feels slightly more academic or structured than a typical conversational interview.

A critical component of the process is often a case study or presentation. You may be given a prompt—sometimes very open-ended (e.g., a single sentence)—and asked to prepare a presentation for a panel. This stage tests your ability to deal with ambiguity, structure a narrative, and defend your product decisions in front of engineers and stakeholders.

This timeline illustrates the typical flow from application to final decision. Use this to pace your preparation: ensure your "tell me about yourself" narrative is polished for the early screens, but reserve your deepest energy for the case study preparation and technical deep dives that occur in the later stages.

5. Deep Dive into Evaluation Areas

Your interviews will dissect your abilities across several core competencies. Based on candidate reports and the specific demands of the role, you should be prepared for deep dives in the following areas:

Product Strategy and Case Execution

This is the core of the onsite or panel stage. Interviewers are looking for your ability to take an ambiguous problem and structure a viable product solution.

Be ready to go over:

• Handling Ambiguity – How you approach a prompt like "Improve the document generation workflow" with limited initial data.
• Prioritization Frameworks – How you decide what to build first when resources are limited (e.g., RICE, Kano).
• Stakeholder Management – How you handle pushback from engineering on feasibility versus design on usability.
• Advanced concepts – Designing specifically for "vertical SaaS" where industry-specific nuance (e.g., legal compliance) is more important than generic mass-market appeal.

Example questions or scenarios:

• "Here is a one-sentence problem statement regarding a user workflow; prepare a presentation on how you would address this."
• "How would you prioritize features for a new AI-based settlement tool?"
• "Walk us through a time you had to pivot a product strategy based on user feedback."

Technical and Data Proficiency

EvenUp is explicit about looking for candidates with excellent quantitative skills. This is not just about reading charts; it is about understanding the data structure.

Be ready to go over:

• Data-Driven Storytelling – Using metrics to justify product decisions.
• Technical Literacy – Understanding the basics of how AI/ML models work (e.g., training data, hallucinations, confidence intervals) so you can collaborate with researchers.
• SQL and Analytics – You may face questions that test your logic regarding data retrieval or metric definition.

Example questions or scenarios:

• "How do you measure the success of an AI feature that generates text?"
• "Describe your experience with SQL and how you use it in your daily work."
• "Basic quiz-style questions on technical product management definitions."

Collaboration and Leadership

You will be interviewed by Engineering Managers and potentially Data/UI engineers. They are assessing if you are a partner they want to work with daily.

Be ready to go over:

• Cross-functional Empathy – Understanding the challenges engineers face and how you support them.
• Conflict Resolution – Specific examples of disagreements with design or engineering and how you resolved them.
• Velocity vs. Quality – How you balance the need to ship fast with the need for high-quality legal outputs.

Example questions or scenarios:

• "Tell me about a time you worked with a difficult stakeholder."
• "How do you handle a situation where the engineering team says a feature is impossible to build within the timeline?"