metaforms · Bengaluru ·
Product Manager at Metaforms (Bengaluru) building AI-native products for market research operations — going end to end from user discovery and problem definition through requirements and engineering delivery. Core focus: designing workflows where AI agents automate survey programming, data processing, and project management with human review loops.
texture · New York Office
triedge-investments · New York City
workiva · USA - Remote
chooose · MA / NY / MD / FL / PA
Metaforms is a vertically integrated AI SaaS platform for market research agencies around the world. The platform helps agencies improve speed, consistency, and delivery quality across research workflows that have historically depended on repetitive manual effort and hard-to-scale specialist knowledge.
The product surface is unusual. Surveys behave like programs, business workflows contain hidden operational nuance, and a small mistake can create real downstream cost for an agency and its clients. That means product thinking here is not about copying familiar SaaS patterns. It is about understanding how work is actually done, identifying where AI agents can reliably help, and shaping systems that feel intuitive to users while introducing genuinely new workflows underneath.
Metaforms is hiring a Senior Product Manager to define and ship new AI-native products and workflow experiences for research operations. This role sits at the intersection of user discovery, domain synthesis, product design, and engineering execution.
The right person is comfortable going end to end, from interviewing users and subject matter experts, to identifying operational bottlenecks, to translating business needs into concrete product requirements, to partnering closely with engineering through delivery.
This is a high-ownership role for someone who likes ambiguous spaces, can reason from first principles, and can build from zero rather than relying on existing category templates.
Conduct frequent user interviews with market research operators, project managers, 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 or systems problems.
Build conviction on where AI agents can remove manual effort, improve turnaround time, or increase quality without disrupting critical trust points.
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 product requirements that are 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.
Translate business requirements into clear engineering asks, including scope, constraints, acceptance criteria, and edge cases.
Partner tightly with engineering, design, operations, and domain experts throughout the build cycle.
Drive prioritization decisions 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.
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 plans with customer success and operators so new workflows land in real agency production including how we measure adoption and efficiency gains.
Product management experience with a strong track record of shipping software products from concept to launch.
Demonstrated ability to operate in ambiguous, early-stage, or zero-to-one product environments.
Gone beyond prompting with AI. At work or on the side, you’ve built a prototype, automated a workflow, or shipped a feature using LLMs, APIs, or agents, and can speak to the trade-offs you ran into.
Strong user discovery skills, including conducting interviews, extracting patterns, and synthesizing findings into product direction.
Comfort working deeply with subject matter experts and operational stakeholders in domains that are initially unfamiliar.
Ability to translate business context and user pain points into clear requirements for engineering teams.
Strong product judgment across scope, workflow design, trade-offs, and iterative delivery.
Comfort owning the end-to-end journey of how a product should be defined, shaped, and built.
Excellent written and verbal communication, especially in cross-functional settings where clarity matters more than volume.
Comfort going deep enough with engineering to write requirements that can be executed without a translator, including constraints, edge cases, and failure modes.
Willingness to reason about AI/agent behavior in production: when to trust automation, when to require human review, and how quality/reliability show up for users.
Bias toward defining and tracking workflow outcomes (speed, quality, trust/adoption), not only shipped scope.
Direct, high-ownership communication under incomplete information clarity over polish, and willingness to call out when process or product direction is wrong.
Users say the product fits how their work actually happens, not how software teams assumed it happened.
Engineering has clear, high-signal product inputs and can move quickly without repeated ambiguity tax.
New workflows feel intuitive enough to adopt, while still delivering meaningful gains in speed, quality, or operational leverage.
Product decisions reflect a strong understanding of both customer outcomes and the realities of production systems.
Shipped workflows show measurable gains in turnaround, quality, or adoption, with clear failure modes understood and managed not just features launched.
Work on AI products that operate in real production environments, not demos or feature theater.
Shape entirely new workflow categories instead of incrementally copying existing tools.
Join a team where product, engineering, and domain understanding are tightly coupled.
Operate with high ownership, fast iteration loops, and direct influence on what gets built.
Via · New York City, San Francisco