The Staff Product Manager will define and build a new portfolio of AI-powered applications, transforming advanced voice AI technology into valuable products for consumers and knowledge workers. This strategic role offers end-to-end ownership of product vision, roadmap, execution, and business performance, with the opportunity to shape an emerging product line from its early stages. You will determine which products to build, identify target audiences, establish differentiation, and develop a sustainable path to profitability. Working closely with engineering, research, design, and marketing teams, you will turn customer insights and technical capabilities into intuitive, market-ready applications. You will lead rapid experimentation, validate product hypotheses, and optimize acquisition, activation, retention, and monetization through measurable outcomes. The role requires a hands-on, AI-native mindset, combining technical fluency, product design intuition, and strong commercial judgment in a fast-moving environment. This fully remote opportunity is ideal for an entrepreneurial product leader who thrives on ambiguity, builds from zero to one, and wants to shape how voice AI transforms everyday software experiences.
Product strategy and vision
Define and own the overall strategy for the AI applications product line, identifying target users, market opportunities, product positioning, and competitive differentiation.
Establish a clear product vision and strategic direction, document key decisions, and communicate recommendations to executive leadership.
Determine which applications to develop, which customer problems to prioritize, and how the portfolio can generate sustainable revenue and profitability.
Maintain strategic focus while incorporating new evidence, customer feedback, technical developments, and evolving business priorities.
Roadmap ownership and product execution
Own the product roadmap, prioritizing features, releases, and investments based on customer value, business impact, technical feasibility, and available resources.
Guide products from early concepts and prototypes through development, launch, iteration, and broader adoption.
Make informed trade-offs regarding scope, timing, functionality, and performance to deliver meaningful outcomes.
Partner with engineering and design teams to translate product requirements into effective, intuitive user experiences.
Use mockups, wireframes, working prototypes, and hands-on product experimentation to communicate ideas and accelerate decision-making.
Business growth and monetization
Build the commercial foundations of the AI applications portfolio, owning acquisition, activation, retention, monetization, and overall business performance.
Define key performance indicators, establish measurable targets, and regularly assess whether products are gaining traction and progressing toward profitability.
Analyze user behavior, conversion funnels, engagement, and revenue performance to identify opportunities for improvement.
Validate metrics and challenge potentially misleading data before making strategic decisions, ensuring product investments are grounded in reliable evidence.
Develop and execute an iterative operating rhythm based on hypotheses, product releases, honest measurement, and subsequent experimentation.
Customer engagement and cross-functional collaboration
Represent the product in discussions with customers and partners, gathering insights and managing commitments regarding product capabilities and delivery timelines.
Work closely with research teams to understand model capabilities and limitations, and with engineering teams to deliver technically robust applications.
Collaborate with design and marketing teams on user experience, product positioning, launches, and go-to-market execution.
Incorporate customer feedback and user research into product decisions, using real-world adoption and usage as primary indicators of success.
Engage internal employees as early users to validate assumptions, identify usability issues, and refine applications before broader releases.
Technical innovation and continuous improvement
Personally prototype concepts, test emerging AI capabilities, and use advanced AI tools to accelerate product development and problem-solving.
Evaluate model performance, latency, privacy, reliability, and platform trade-offs in collaboration with technical teams.
Identify where AI can improve user experiences, understand where models may fail, and establish appropriate methods to validate outputs.
Promote a culture of experimentation, continuous learning, and evidence-based decision-making in a rapidly evolving AI landscape.