Sr. Tech AI/ML Product Manager - Job Hunt Engine

About JobHire.AI

JobHire.AI is building a vertical AI agent that automates job search for professionals. We help thousands of users land interviews by finding, tailoring, and applying to jobs on their behalf — at scale and with precision. We’re profitable, growing fast, and now entering a phase of deep product refinement and organic growth through exceptional UX and perceived value.

~35% MoM; top 1% in growth rate

Profitable from day one

40 people

Investors: Deel Ventures, Daniel Gutenberg, Dave Waiser, Margulan Seisembayev, and other unicorn founders.

Mission

JobHire.AI is a personal AI agent for continuous professional development and happiness at work

About the Role

We are seeking a highly analytical and hands-on Senior Tech AI/ML Product Manager with deep expertise in Machine Learning and Artificial Intelligence and Product Management. You will define and build a best-in-class job discovery and matching engine that connects users with the most relevant roles for them, at scale. You will own the strategy, discovery, and delivery of AI-powered features, focusing on matching, ranking, and personalization systems. The ideal candidate is an entrepreneurial thinker with an engineering mindset, capable of building rapid prototypes, making decisive calls with imperfect data, and relentlessly driving measurable outcomes.

Key Responsibilities

• Strategy & Ownership: Define the vision, strategy, and roadmap for AI/ML product features (JobHunt Engine). Take full ownership of the product lifecycle from hypothesis to scaled impact, focusing on business results, not just model performance.
• ML Product Leadership: Translate business problems into ML hypotheses and solutions. Work side-by-side with ML engineers and data scientists to define data requirements, evaluation frameworks (evals, RAG, agents), model monitoring, and delivery processes.
• Hypothesis Validation & Experimentation: Design and execute rapid, pragmatic validation cycles. Formulate clear hypotheses (Problem → Mechanism → Impact → Metric), choose the right validation method (A/B test, shadow model, phased rollout), and make data-driven go/no-go decisions under uncertainty. Be scrappy and effective with limited data or infrastructure.
• Structured Problem Solving: Apply critical thinking to decompose complex, ambiguous problems. Cut through noise, prioritize what truly matters, and build simple, effective solutions first.
• Cross-Functional Execution: Collaborate closely with Engineering, Data Science, and business teams. Communicate complex ML concepts clearly and align stakeholders on goals, trade-offs, and progress.

Expected Outcomes

First 3 Months

• Establish baseline measurement of job supply coverage across the U.S. market, including companies listed in the NASDAQ-100
• Increase the percentage of users who successfully find a job through the platform by 50%, driven by improvements in matching logic and job relevance

First 6 Months

• Expand job vacancy coverage in the U.S. market, achieving up to 80% coverage of NASDAQ-100 companies and increasing overall coverage by 30%
• Ship a major upgrade to the resume enhancement feature
• Increase the percentage of users who successfully found a job through our platform by 3×

12 Months

• Further expand U.S. job vacancy coverage, achieving a 50% increase in coverage of NASDAQ-100 companies compared to the 6-month baseline
• Double the matching success rate, measured as the percentage of matched vacancies approved as relevant by users
• Improve the application-to-offer conversion rate by 50%, directly impacting the core business outcome — successful employment