AI Technical Product Manager

Help shape AI features: Work with the Product Lead to translate the QV.AC vision into concrete use cases, feature proposals, and product requirements. Bring ideas grounded in user needs, research, and hands-on experimentation.

Understand users and their problems: Gather feedback, conduct research, and explore how people use AI in their everyday workflows. Identify where AI can solve a meaningful problem and where a simpler solution may work better.

Turn ideas into clear specifications: Write product briefs, user stories, acceptance criteria, and user flows that help engineering and design understand the intended experience, constraints, and measures of success.

Prototype and explore: Create wireframes, mockups, and working prototypes to test ideas and communicate product behavior. Use AI-assisted tools to explore concepts quickly and refine them with the team.

Collaborate on technical trade-offs: Work with engineers to understand what is feasible across devices and models. Help assess the impact of response quality, latency, memory usage, battery consumption, and privacy on the user experience.

Support prioritization and planning: Help the Product Lead evaluate opportunities, clarify scope, identify dependencies, and maintain a focused backlog aligned with product priorities.

Drive feature delivery: Follow assigned features through development, resolve product questions, review the experience, and coordinate feedback and release readiness with relevant teams.

Stay close to AI developments: Explore new AI products, models, tools, and interaction patterns, and explain their practical relevance to QV.AC. Share clear recommendations about what is worth testing or adopting and why.

Job requirements

Desired Candidate Profile

7+ years of product management experience, ideally working on AI software products with technical engineering teams.

Practical experience building, shipping, or prototyping AI-powered features, with the ability to explain your contribution and what you learned.

A working understanding of AI concepts such as inference, harnesses, model capabilities and limitations, context, and evaluation, alongside an interest in local and on-device AI.

The ability to translate an ambiguous idea into a clear user problem, proposed solution, and actionable requirements.

Strong product judgment, with attention to usability, scope, and measurable user value.

Confidence creating prototypes using design or coding tools

Clear written and verbal communication, including the ability to discuss technical trade-offs with engineers and explain them to non-technical colleagues.

A proactive, collaborative approach: comfortable taking responsibility for your work, asking questions early, and incorporating feedback.

Familiarity with product analytics, user research, and iterative software development.

Preferred:

Experience with local AI, on-device inference, or open-source models.

Experience building AI assistants, conversational interfaces, or multimodal features.

Familiarity with desktop or mobile product development.

An understanding of peer-to-peer technologies and decentralized applications.

Experience evaluating AI output quality and designing user experiences that account for uncertainty and errors.

Experience working in a distributed team.