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About This Role
EvenUp is on a mission to close the justice gap using technology and AI. We empower personal injury lawyers and victims to get the justice they deserve. Our products enable law firms to secure faster settlements, higher payouts, and better outcomes for victims injured through no fault of their own in vehicle collisions, accidents, natural disasters, and more.
We are one of the fastest\-growing vertical SaaS companies in history, and we are just getting started. EvenUp is backed by top VCs, including Bessemer Venture Partners, Bain Capital Ventures, SignalFire, and Lightspeed. We are looking to expand our team with talented, driven, and collaborative individuals who seek to have a lasting impact. Learn more at www.evenuplaw.com.
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. 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.
This is a hybrid role with the expectation of working at least 3 days a week from our San Francisco or Toronto offices. Title and compensation to be determined based on candidate experience.
What you'll do:
- 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.
What we look for:
- 5\+ years of product management experience, including time at a high\-growth startup (Series B–E).
- 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.
- 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).
Bonus points for:
- 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.
Benefits \& Perks:
As part of our total rewards package, we offer attractive benefits and perks to our employees, including:
- Choice of medical, dental, and vision insurance plans for you and your family.
- Additional insurance coverage options for life, accident, or critical illness.
- Flexible paid time off, sick leave, short\-term and long\-term disability.
- 10 US observed holidays, and Canadian statutory holidays by province.
- A home office stipend.
- 401(k) for US\-based employees and RRSP for Canada\-based employees.
- Paid parental leave.
- A local in\-person meet\-up program.
- Hubs in San Francisco and Toronto.
*(Please note the above benefits \& perks are for full\-time employees)*
*Notice to Candidates:*
*To ensure fairness and proper consideration, we do not accept resumes or expressions of interest via email or social media messages. If you’re interested in a role, please submit your application directly through our* *careers page**.*
*Please note that EvenUp may use AI notetakers and other recording devices in the recruiting process. If you interview with us, with your consent, we may record your conversations and summarize them into notes for internal use. Recording is optional, and declining will not affect your candidacy.*
*EvenUp is an equal opportunity employer. We are committed to diversity and inclusion in our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.*
Compensation Range: $177,650 \- $209,000
Salary Context
This $177K-$209K range is above the median for AI Product Manager roles in our dataset (median: $185K across 167 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At EvenUp, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $217,100 based on 471 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($193K) sits 11% below the category median. Disclosed range: $177K to $209K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
EvenUp AI Hiring
EvenUp has 2 open AI roles right now. They're hiring across AI Product Manager. Based in San Francisco, CA, US. Compensation range: $209K - $260K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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