Technical Product Manager, FutureFit

• You know this market. You understand how workforce agencies operate, what job seekers actually experience navigating employment and training systems, and what the policy environment (WIOA, Workforce Pell, ETPL) means for product design. This is the hardest thing to hire for and the most important
• 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. You have a working knowledge of what different models are good at, where they fail, and how to orchestrate them to get real work done
• You build. You are a player-coach by disposition. You do not wait for a designer to mock something up or an engineer to prototype an idea. You get your hands dirty, generate artifacts yourself, and treat every step between “customer problem” and “working thing to react to” as your responsibility to compress
• You are technically fluent, especially in data. You do not need to write production code, but you need to be able to pull data from BI tools and data systems and leverage Claude Code to self-sufficiently understand system architecture, engage in ADR conversations, and translate technical constraints into product decisions
• You write precisely. PRDs, specs, and tickets from you should need minimal back-and-forth. You can make the implicit explicit and leave no ambiguity about what “done” looks like
• You are a strong customer researcher. You know how to run a useful interview, synthesize qualitative signals into testable hypotheses, and separate what users say from what they need

Desirable

• The TPM — Talent Journey owns the experience of every person who walks through a FutureFit AI-powered portal looking for work, training, or a better career path
• 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. Every person on the product side is an active builder, not just a coordinator. This role is part product manager, part project manager, part prototyper, part researcher — and increasingly, part AI engineer
• We expect this person to 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 own the end-to-end talent experience, including the AI Coach, Career Passport, and the training and job moving surfaces that connect people to real next steps. You will be close enough to the engineering work to translate an ADR into a sprint’s worth of precise tickets
• The best candidate for this role may have been a workforce counselor, a case manager, a researcher, a UX designer, a data analyst, or a scrappy generalist at a workforce tech startup. Prior PM titles are not required
• What matters is that you know this space, you build with AI and speed, and you keep the person at the center of every decision
• Talent Portal: End-to-end portal experience across onboarding, profile-building, training discovery, job matching, and progress tracking — for job seekers served by state workforce agencies, American Job Centers, and employer-sponsored programs
• AI Coach: Product direction for FutureFit's conversational AI coach — defining what it knows about a user, when it surfaces the right nudge, and how it improves over time through richer user profiling and feedback loops
• Career Passport: The longitudinal profile layer that captures skills, credentials, experiences, and outcomes across a job seeker's journey — the data flywheel FutureFit's entire platform depends on
• Training & Job Matching Surfaces: The search, recommendation, and eligibility-aware features that connect job seekers to training programs, apprenticeships, and open roles based on their actual profile and goals
• Talent-Side Data & AI Use Cases: Identify and define the LLM, recommender, and signal-capture opportunities within the job seeker journey that feed the platform's outcomes intelligence layer
• Use AI agents and LLM tooling (Claude, Cursor, v0, and equivalents) to do work that would otherwise require a full design sprint, from generating a working prototype to drafting a test plan to synthesizing a stack of customer interviews
• Operate independently when needed: run discovery, generate design options, write specs, draft tickets, and validate with users in a compressed cycle that does not wait for full team availability
• Know when to manage a fleet of agents and when to stop and involve humans. The judgment about what AI can accelerate and what still requires engineering architecture is as important as the tooling itself
• Set the bar for AI-native product workflow on the team. What you figure out about using agents, automation, and LLM tooling in the product cycle should become a reusable process that others adopt
• Conduct continuous, lightweight customer research (direct interviews, session reviews, portal data analysis) and synthesize findings into crisp problem statements and design hypotheses
• Prototype fast. Use AI tooling, no-code and low-code methods, and collaborative sessions with engineering to get something testable in front of real users before writing a PRD. Use AI-assisted tools (Cursor, Claude, Figma AI, and equivalents) to generate working prototypes, draft user flows, and validate hypotheses before committing engineering capacity
• Run structured discovery sessions that surface what job seekers actually need to navigate the next step in their career journey
• Stay current on workforce development policy, WIOA service delivery, Workforce Pell, and Eligible Training Provider List (ETPL) dynamics
• Translate architecture decision records (ADRs) and engineering context into precisely scoped tickets; you do not need to write code, but you need to understand enough about how the system works to know what needs to happen and in what order
• Write PRDs and specs that are useful to engineers with clear acceptance criteria, explicit edge cases, defined data requirements, and a testable definition of done
• Own the multi-quarter roadmap for the Talent Journey domain, connecting individual feature decisions to FutureFit’s broader flywheel: richer profiles → better movement → measurable outcomes → platform defensibility
• Contribute to quarterly product strategy discussions alongside the CPO, Director of Data, VP of Engineering, and Sr. TPM for Customer Workflows, with clear representation of the job seeker perspective in every prioritization conversation
• Translate company strategy into a coherent sequencing plan for your domain, clarifying what to build first, what to defer, and why
• Drive competitor and market analysis for job seeker facing workforce technology, with a particular eye on AI-native entrants and state-level digital transformation efforts
• Define the user profiling strategy for AI Coach; what data the coach collects and how it feeds agentic, movement and recommendation systems downstream
• Set the evaluation framework for AI Coach quality: what “good” looks like for a job seeker interaction, how you measure it, and how you create a feedback loop with the data team to improve it
• Identify and prioritize Career Passport data capture opportunities, from assessment integrations to training completions to employer signals, that increase the density and accuracy of the job seeker’s longitudinal record
• Keep job seeker privacy and trust at the center of every data design decision, flagging risks proactively and partnering with engineering and legal on compliant implementation
• Be the product voice for job seekers in every planning and prioritization conversation
• Partner with the engineering lead from ADR to sprint planning, serving as the connective tissue between what the customer needs and what the system can actually deliver
• Support the data team on job seeker–side analytics: help define what gets measured, how, and what questions the warehouse needs to be able to answer
• Manage launch readiness end-to-end within your domain: coordinate CS and GTM readiness, define rollout criteria, and own the feedback loop post-launch
• Partner with Customer Success and GTM to enable state agency and employer customers with the job seeker product narrative, because how the portal serves job seekers directly impacts contract renewal and expansion
• Work closely with the Sr. TPM of Customer Workflows to ensure the job seeker portal and employer/recruiter portal are designed as a coherent system, not two separate products

• 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
• Panel Interview
• Final 1:1 Interviews
• Final Decision
• Generally, this entire process takes around 6 weeks, although the timing can vary due to specific candidate circumstances