Senior Technical Product Manager — Customer Workflow

About the role

We are seeking a Senior Technical Product Manager to own the Customer Workflows domain. This role is part product manager, part project manager, part prototyper, part researcher, and increasingly, part AI engineer.

Responsibilities

• Experience, performance, and activation across all recruiter-facing surfaces, including feature instrumentation and health metrics.
• End-to-end workflows powering business service operations on the platform.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Requirements

• 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.
• Promised cross-functional credibility: Sales, CS, and Engineering see you as a trusted partner, not a gatekeeper.

Qualifications

• Education: Your grit, hunger, and drive are more important than your alma mater. If you learn continuously, tackle challenges head-on, and know your strengths and gaps intimately, you’re our person.

Skills

• 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.
• AI-native: you already use LLMs, agents, and AI-assisted tools as your primary work layer — not occasionally, not experimentally.

Benefits

At FutureFit AI, we are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, gender identity, sexual orientation, age, disability, veteran status, or other applicable legally protected characteristics.

Pay

The base salary band for this role is USD $125,000 to $160,000 for candidates based in the US and CAD $125,000 to $165,000 for candidates based in Canada, regardless of location.

Schedule

This role is remote, but you may be expected to travel up to once per quarter for offsites and team gatherings.