Senior Technical Product Manager, FutureFit

• Data shows that men typically apply when meeting 3/10 requirements, while women often wait until it's 10/10. We encourage you to apply if you see a strong (not necessarily perfect) fit
• Demonstrated ownership of a product area at a platform level — you can articulate the what, why, and trade-offs of every major decision in your domain with authority
• A track record leading multi-quarter, cross-functional technical initiatives end-to-end
• You are AI-native, not AI-curious: you already use LLMs, agents, and AI-assisted tools as your primary work layer — not occasionally, not experimentally — and you know what different models are good at, where they fail, and how to orchestrate them to get real work done
• Strong technical depth: you can shape architecture discussions, evaluate agentic AI/LLM system quality, define evaluation frameworks, and scope non-functional requirements independently
• Data fluency: you define OKRs and success metrics with precision, maintain a living metrics view, and drive data-informed decisions — not just report on delivery velocity
• Proven cross-functional credibility: Sales, CS, and Engineering see you as a trusted partner, not a gatekeeper
• Your alma mater isn't our focus. Your grit, hunger, and drive are. If you learn continuously, tackle challenges head-on, and know your strengths and gaps intimately, you're our person

Desirable

• We're seeking a Senior Technical Product Manager to own the Customer Workflows domain — the full experience of how employers, recruiters, business service teams, and staffing agents experience and interact with our technology
• This is not a traditional product management role
• We are not looking for someone who has spent years in PM org hierarchies, handed off specs to designers, and waited for engineers to ship
• We are building something different
• FutureFit AI operates in small, high-context teams, and every person on the product side is an active builder
• This role is part product manager, part project manager, part prototyper, part researcher, and increasingly, part AI engineer
• You will learn to manage fleets of agents, ship working prototypes with AI tooling, and collapse the distance between “I heard a customer problem” and “here is something you can use” from weeks to days
• You will operate at the intersection of product execution, platform architecture, and data intelligence: leading initiatives, owning your domain's roadmap end-to-end, and delivering against the craft bar across product-engineering-data collaboration
• Employer & Recruiter Portal: Experience, performance, and activation across all recruiter-facing surfaces, including feature instrumentation and health metrics
• Business Service Workflows: End-to-end workflows powering business service operations on the platform
• Agentic Workflows: Tools, flows, and experiences that agents use to deliver value to employers and admins — increasingly augmented by agentic AI that handles routine steps and surfaces the next best action
• Agentic AI & data use cases: Surface and define agentic AI, LLM, search, and recommender applications — including candidate recommendations and automated business-service flows — translating them into clear requirements and the evaluation standards the data and engineering teams adopt
• AI-native execution: Use AI agents and LLM tooling as your default work layer — generating working prototypes, drafting test plans, synthesizing customer interviews — and turn what you learn into reusable processes others adopt. Know when to manage a fleet of agents and when to stop and involve humans
• Customer-centered discovery: Run continuous, lightweight research (direct interviews, session reviews, portal data analysis), structured Jobs-to-Be-Done discovery sessions, and fast prototyping to get something testable in front of real users before committing engineering capacity
• Product strategy & roadmap: Author and maintain quarterly roadmaps for the Customer Workflows domain, contribute to strategy discussions alongside the CPO, VP of Engineering, and VP of AI & ML, and connect employer/recruiter outcomes to ARR and mission goals — informed by competitor analysis, with a particular eye on agentic AI-native entrants
• Technical excellence: Own trade-offs across performance, scalability, cost, and speed; translate ADRs and engineering context into precisely scoped tickets; write PRDs with clear acceptance criteria, explicit edge cases, defined data requirements, and a testable definition of done; partner with engineering and analytics leads on data warehouse initiatives such as large-scale integrations and ATS work
• Cross-functional leadership: Be the go-to product partner for Product, Engineering, Data, Customer Success, and GTM in your domain; proactively brief Sales and CS before major releases; mentor peers on technical scoping, evaluation frameworks, agentic AI workflow, and discovery craft; and create reusable templates, decision frameworks, and playbooks that raise the quality floor

• At FutureFit AI, our hiring process is designed to help you assess whether this role and our culture are the right fit based on your unique skills, mindset, and experiences. We move fast and work with intensity, so we want you to get a real sense of that from the start
• Each journey includes a mix of interviews and a performance challenge. For this role, that might look like:
• Online Application
• Initial Screen with Director of People & Culture
• Interview with Hiring Manager
• Performance Challenge
• Final 1:1 Interviews
• Final Decision
• Generally, this entire process takes around 6 weeks, although the timing can vary due to specific candidate circumstances