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. 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.

3. 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.

4. 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?"

The word cloud above highlights the most frequently discussed themes in EvenUp interviews. Note the prominence of AI, Experience, and Presentation. This indicates that while your resume gets you in the door, your ability to present a cohesive strategy involving AI concepts is what will likely secure the offer.

5. Key Responsibilities

As a Product Manager at EvenUp, your day-to-day work is a blend of strategic planning and hands-on execution. You are responsible for understanding both internal and external user needs through rigorous qualitative and quantitative research. You will spend significant time analyzing how legal professionals interact with your software to identify friction points in their document generation and negotiation workflows.

Collaboration is central to this role. You will lead cross-functional sprints, working side-by-side with engineers, designers, and machine learning researchers. You are expected to be the bridge that translates complex AI capabilities into accessible user features. This means you aren't just writing tickets; you are actively shaping how the AI solves specific legal problems, ensuring the output is accurate and valuable.

Furthermore, you play a key role in the go-to-market phase. You will collaborate with Product Marketing, Sales, and Customer Success to ensure that new feature launches are seamless. You are accountable for the success of your products, which requires you to constantly monitor performance metrics, iterate on designs based on feedback, and influence the broader company strategy to maintain high velocity.

6. Role Requirements & Qualifications

To be a strong contender for this role, you need to meet specific professional benchmarks that demonstrate your ability to handle the complexity of the position.

• Technical & Data Skills – Proficiency in SQL is highly valued, as is a strong grasp of data analytics. You must be comfortable working with technical teams, specifically in an AI/ML context.
• Experience Level – The role typically requires at least 3 years of experience in B2B product management. Experience in a startup environment is preferred because it demonstrates an ability to work with agility and autonomy.
• Soft Skills – You need "execution-driven" leadership skills. This includes the ability to influence without authority, communicate complex ideas clearly to non-technical stakeholders, and maintain high accountability.

Must-have skills:

• Experience with complex workflow products (e.g., document generation, decision support).
• Strong qualitative user research capabilities.
• Demonstrated ability to prioritize and iterate quickly.

Nice-to-have skills:

• Direct experience building AI/ML products.
• Background in the legal tech industry or vertical SaaS.
• Experience with "artifact generation" tools.

7. Common Interview Questions

The following questions are representative of what you might encounter. They are drawn from candidate data and the specific requirements of the role. Note that questions can vary significantly depending on who is interviewing you (e.g., an Engineering Manager might ask different questions than a Product Lead).

Experience & Behavioral

• "Tell me about your experience with product management, specifically in a B2B context."
• "What is your experience working with AI or Machine Learning models?"
• "Describe a time you had to learn a new domain or industry quickly."
• "Why do you want to work in the legal tech space specifically?"

Case Study & Problem Solving

• "We want to improve the time-to-settlement for our clients. How would you investigate this problem?"
• "Here is a one-sentence prompt: 'Design a feature to help lawyers draft demand letters faster.' Prepare a short presentation."
• "How would you prioritize a roadmap if you have requests from Sales, Engineering, and current customers that all conflict?"

Technical & Quiz-like

• "Define [Specific Product Term] and explain how you apply it."
• "How would you validate a hypothesis using data? Walk me through the query logic you might use."
• "What are the limitations of current LLMs when applied to legal document generation?"

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.

8. Frequently Asked Questions

Q: Is this role remote or hybrid? The position is a hybrid role. You are expected to work from one of the office hubs (San Francisco or Toronto) at least 3 days a week. This facilitates the close collaboration required with engineering and design teams.

Q: How technical do I need to be? While you don't need to write production code, you do need to be "data-savvy." The job description explicitly mentions a passion for SQL. Additionally, because the product relies heavily on AI, you need to understand the capabilities and limitations of ML to effectively manage the product roadmap.

Q: What is the "Case Study" format? Candidates have reported receiving a prompt (sometimes very brief) ahead of a panel interview. You are expected to create a presentation. The key here is not just the solution you propose, but how you structure your thinking, define the problem, and present your narrative to a group of engineers and stakeholders.

Q: How long does the process take? The process is generally efficient, often taking about 2 to 4 weeks from the initial screen to a final decision. However, this can vary based on scheduling availability for the panel rounds.

9. Other General Tips

Prepare for the "One-Sentence" Case Study: Several candidates have noted receiving very brief prompts for their case study presentation. Do not let the lack of detail throw you off. This is a test of how you deal with ambiguity. Make reasonable assumptions, state them clearly at the start of your presentation, and build your strategy on top of them.

Know the Mission: EvenUp is mission-driven. Understanding the "Justice Gap" and how technology can help personal injury victims is crucial. Weave this mission into your answers to show cultural alignment.

Engage with the Engineers: During your panel, you may speak with Data or UI engineers. They are looking for a PM who respects their craft. When presenting your case, explicitly mention how you would work with engineering to assess feasibility and technical debt.

10. Summary & Next Steps

The Product Manager role at EvenUp is an opportunity to define the future of legal technology. You will be working on high-impact products that use AI to level the playing field for injury victims. This is a role for a builder—someone who loves complex workflows, data-driven insights, and the fast pace of a vertical SaaS startup.

To succeed, focus your preparation on three pillars: AI/Data fluency, User Research, and Structured Problem Solving. Be ready to present your ideas clearly and defend them with logic and metrics. Review your SQL basics, practice breaking down ambiguous product prompts, and ensure you can articulate your experience with AI clearly.

The compensation range for this role is $127,500 – $218,200 USD. This wide range suggests that the final offer depends heavily on your specific level of experience, location, and performance during the interview process. Be prepared to discuss where your experience places you within this band, keeping in mind the high value placed on specialized AI and B2B SaaS experience.

You have the roadmap; now it is time to execute. Approach the process with confidence, curiosity, and a clear focus on the value you can bring to the EvenUp mission. Good luck!