Metaforms provides AI agents that automate market-research operations—questionnaire-to-code conversion, data quality flagging, panel vendor coordination, quota tracking—and integrates with tools like Decipher, SPSS, and Confirmit.
About Metaforms At Metaforms, we're redefining how market research gets done. Market research still runs on legacy survey platforms, manual project operations, fragmented handoffs, and domain expertise locked in a few specialists' heads. Metaforms is building the agent layer that makes this work dramatically more efficient — without lowering the bar for quality, accuracy, or client trust. Our vertically integrated AI platform empowers agencies like Dynata, Savanta, and Borderless Access to scale 10x faster, with AI Agents augmenting survey programming, data processing, and project management. We process 1,000+ surveys monthly, serve agencies working with Fortune 500 companies, and are backed by $9M in Series A funding to aggressively scale. The product surface here is unusual. Surveys behave like programs, business workflows contain hidden operational nuance, and a small mistake creates real downstream cost for an agency and its clients. Product thinking at Metaforms isn't about copying familiar SaaS patterns — it's about understanding how work actually gets done, identifying where AI agents can reliably help, and shaping systems that feel intuitive to users while introducing genuinely new workflows underneath. The Role We're hiring a Product Manager to define and ship new AI-native products and workflow experiences for research operations. The role sits at the intersection of user discovery, domain synthesis, product design, and engineering execution. You'll go end to end — from interviewing users and subject matter experts, to identifying operational bottlenecks, to translating business needs into concrete requirements, to partnering closely with engineering through delivery. This is a high-ownership role for someone who thrives in ambiguous spaces, reasons from first principles, and builds from zero rather than relying on existing category templates. What You'll Own User Discovery & Problem Definition - Conduct frequent interviews with research operators, project managers, survey programmers, and agency leaders to understand how work actually happens - Turn messy qualitative input into clear product insights, problem statements, and opportunity areas - Work closely with subject matter experts to separate surface requests from deeper workflow and systems problems - Build conviction on where AI agents can remove manual effort, improve turnaround, or raise quality — without disrupting critical trust points Product Design & 0→1 Development - Define new product concepts, workflow patterns, and operating models for AI-assisted research work - Design familiar-feeling user journeys that introduce new capabilities without overwhelming users - Write requirements specific enough for engineering to execute, while preserving room for iteration where the solution space is still emerging - Iterate rapidly on prototypes, internal feedback, and early customer usage to converge on products that are both useful and operationally viable Cross-Functional Execution - Translate business requirements into clear engineering asks — scope, constraints, acceptance criteria, and edge cases - Partner tightly with engineering, design, operations, and domain experts through the build cycle - Drive prioritization by balancing customer value, technical feasibility, reliability risk, and speed - Ensure the team is solving the right problem, not just shipping the fastest version of the first idea Product Quality & Adoption - Define what "good" looks like for product outcomes, user trust, and workflow adoption - Identify failure modes in user flows, handoffs, and agent behavior — especially in high-consequence operational steps - Close the loop between shipped behavior, customer feedback, and roadmap decisions - Own rollout, onboarding, and change management with customer success and operators so new workflows land in real agency production — including how adoption and efficiency gains are measured Representative Problems You Might Work On - Designing an AI-assisted workflow that turns a raw client questionnaire into an executable survey build process - Creating review and approval loops where human operators guide or validate agent output without adding unnecessary friction - Reworking a fragmented project-management flow into a single experience that makes bottlenecks, state, and exceptions visible - Defining how internal subject matter experts collaborate with product and engineering to encode operational knowledge into a scalable system - Building product surfaces that feel familiar to agency teams while delivering step-change improvements in speed and efficiency What We're Looking For Must-have - Proven product management experience shipping software from concept to launch - Demonstrated ability to operate in ambiguous, early-stage, or zero-to-one environments - Strong user discovery skills — interviewing, pattern extraction, and synthesis into product direction - Comfort going deep with subject matter experts and operational stakeholders in initially unfamiliar domains - Ability to write requirements engineering can execute without a translator — including constraints, edge cases, and failure modes - Strong product judgment across scope, workflow design, trade-offs, and iterative delivery - Willingness to reason about AI/agent behavior in production: when to trust automation, when to require human review, and how quality and reliability show up for users - Bias toward defining and tracking workflow outcomes (speed, quality, trust, adoption) — not just shipped scope - Excellent written and verbal communication; direct, high-ownership style under incomplete information — clarity over polish, and willingness to call out when direction is wrong Nice-to-have - Experience building AI-native, automation-first, or workflow software — especially human-in-the-loop or reliability-sensitive systems - Experience partnering close