Staff Product Manager, AI Agents & Assistant

As our Staff Product Manager for Agentic Capabilities, you will own the vision, strategy and roadmap for how Dashlane becomes an interface for AI agents and assistants for consumers and businesses. Success means people hand real, valuable tasks to agents through Dashlane and keep doing it, and companies trust it enough to let their teams do the same.

You will own the product alongside senior engineers as a Staff-level individual contributor in a 0-to-1 role where standards are still unsettled. We are looking for strong familiarity with the modern AI landscape, including LLMs, assistants and agentic workflows, and sound judgment about what agents can and cannot be trusted with.

At Dashlane, we embrace a hybrid culture that combines the best of both worlds: the creativity and energy of in-person collaboration with the flexibility of remote work. Our model is designed to strengthen team connections while supporting individual productivity and work-life balance. To maximize collaboration, we come together in the office on Mondays, Tuesdays, and Thursdays, while Wednesdays and Fridays offer more flexibility for focused work.

• Set the direction. Form and defend a point of view on how agents and assistants should act with what Dashlane protects, for consumers and businesses, and turn it into a strategy, positioning, and multi-year plan that leadership will back.

• Read the market and judge readiness. Keep a living view of how agent makers, assistants, browsers, identity vendors and password managers are moving. Assess how ready emerging pieces such as passkeys for agents and agent sign-in standards are, and set the triggers for acting on each.

• Own the evidence. Run discovery on the jobs people hand to agents at home and at work. Establish who hands over real tasks and what holds them back, so your bets follow demand, not hype.

• Connect consumer and business. Treat both as a connected problem. Turn what we learn from individuals handing tasks to agents into the visibility and controls that IT and Security teams need to govern agent access at work. Flag when a decision for one audience affects the other.

• Own the trust commitment. Define what Dashlane promises about what an agent or model can see and do, and hold every product decision and release to that promise. Turn what agents can and cannot be trusted with into a set of durable product primitives, such as limits, approvals, records and revocation, that stay valid as agents change.

• Shape our place in the ecosystem. Decide which agent makers and platforms we invest in and on what terms. Build the relationships and technical case that make Dashlane an easy yes for them, and for the services agents log in to. Lead Dashlane’s input to the standards that govern how agents sign in and act on someone’s behalf, so our positions reflect real customer needs and our product direction.

• Own the numbers. Set baselines and targets for adoption, repeat use and trust, including time to a first approved agent login, how long people take to decide on an agent’s request, and how often failures are recovered without the person starting over. Be accountable for them to leadership.

• Align the organization. Bring Design, Engineering, Security, Legal, Sales & Marketing towards one plan, establish the operating rhythm and decision framework for the team, and settle ownership where it is unclear.
  
  

In your first year:

• By 90 days, the team's early engineering work has become a shared view of the problem, a trust model that Security and Architecture back, and proposed targets for what "working" means. You have shared a point of view on the category with Dashlane leadership.

• By 6 months, real people are experiencing agentic value through Dashlane, engagement is recurring, and you know who gets the most value first.

• By 12 months, people keep using it and it shows in who chooses and stays on our plans. A version for companies has a clear first slice, and you have a view on where to go next. You have a vision and roadmap for agents and assistants across B2B and B2C that leadership has backed.

• 8+ years in product management, including ownership of an authentication, security, identity or permissions-heavy product, and at least one product you took from zero to market with measurable success (adoption, revenue or retention) in a market that was still emerging.

• Enough technical fluency (OAuth, passkeys, sessions, browser extensions) to follow a token-flow discussion with engineers and ask where it breaks. You don't need to write code but you must be comfortable with speaking the language of engineering.

• Strong familiarity with the modern AI landscape, including LLMs, assistants and agentic workflows. The ability to reason about what AI can and cannot do through a product and customer lens, with working knowledge of how agents operate: tool use and standard tool interfaces, browser-driving agents, how context is kept or lost, and where they can be tricked.

• A track record of reading a fast-moving market and turning its signals into a strategy that leadership acted on.

• Experience running 0-to-1 discovery with small samples and incomplete information, defining success as customer outcomes rather than shipped features.

• AI-native ways of working: you build working prototypes with AI, run discovery synthesis through it, chain agents into your own workflow, and can show concretely how it has changed how you “do product”.

• Strong written and verbal communication skills in English: crisp problem statements, clear 1-pagers, compelling roadmap rationale, and external narratives that shape how a category is understood.

• Systems thinking and platform experience. You think in systems and have shipped a platform, API, integration product or partner ecosystem that other software relies on, with outcomes you can describe: the baseline, what changed, what you cut.

• Consumer and business experience. Experience with products that serve both individuals and businesses, and the instinct to flag early how a decision in one ripples into the other.

• Comfort with ambiguity. You will join a team whose engineers have already started scouting ahead, and you favor action over perfection, deciding with incomplete information.

• A collaborative spirit and low ego. As comfortable deferring to others' expertise as you are asserting your own point of view.

• Intellectual honesty. You share results transparently, learn openly from failures, and write down the evidence that would make you move earlier, later or not at all.

• Security standard for AI. You have a threat-model instinct (prompt injection, phishing, malicious extensions, what a compromised agent could still do) and hold AI to the same standard as security: human accountability at every consequential decision, and evaluation before anything ships.