Product Manager, AI Native Initiatives

Mission 🚀

At Faros, we are building the engineering intelligence platform for the AI era. We help engineering organizations understand the impact of AI on software delivery while providing the operational context that enables AI agents to operate with greater accuracy, relevance, and trust in complex enterprise environments. Our platform is used by some of the most sophisticated engineering teams in the world, ranging from fast-growing technology companies to F500 enterprises. We believe the future of software development will be built by humans and AI working together, and we're building the foundation that makes that possible.

About the Role

This is a role for a seasoned, hands-on Product Manager, who wants to be in the work, not above it. You will own a meaningful surface area of our new AI-native product line. Possible surfaces include the agent–human collaboration layer (how engineers review, steer, and approve agent work), the AI/eval layer (model selection, eval frameworks, quality/cost/latency trade-offs), or the integration layer, where this product integrates with our existing Faros platform.

For the next 12 to 18 months, you will personally write requirements, craft prototypes with modern AI tools, sit in architecture conversations with senior ICs, and be the person who decides what ships and when in. You will be in customer conversations every week. You will set the bar for what "awesome" looks like and be accountable for it.

You will work shoulder to shoulder with our Head of Product, Head of Design, Tech Leads and senior engineers on this team, and have direct exposure to our founders and our broader leadership team. The PDE triad — Product, Engineering, and Design — operates as equal peers at Faros. You will hold a distinct, unapologetic product voice in every conversation.

This is not a coordination role and not a process role. If your last few years have been mostly running rituals, herding tickets, and writing decks, this one will feel off.

What You'll Do

• Own a surface end-to-end. Define the wedge, craft the requirements, bring them to life, ship the first version, and iterate to product–market fit. Success is measured in customer outcomes — usage, repeat engagement, CSAT, design-partner-to-paid conversion — not features shipped.

• Prototype and ship yourself. Modern AI tools have rewritten what a single PM can do. We expect you to take full advantage of it. Build clickable prototypes using the latest and greatest tools. Spin up a working agent in a notebook to test a hypothesis. Put working software in front of customers in days, not sprints. If you have not touched a codebase or a model API in years, this might not be the role for you.

• Make hard technical tradeoffs. Agentic systems force daily decisions across quality, latency, cost, reliability, and user trust. You will be in the room for those tradeoffs in your area, push back when something does not feel right, and be able to explain the call to your team, your customers, and our leadership team.

• Design for human-in-the-loop agentic systems. Every design decision has to account for where agents are strong, where they fail, what the Faros context brings to the table, and how humans stay meaningfully in the loop without becoming the bottleneck. You will have an opinion on what that should feel like, and the evidence to back it up.

• Run the eval and learning loop. What "good" looks like for an agent product is not obvious, and it changes as models change. You will help us define the evals, instrument the product, read the traces, and translate what you see into the next iteration.

• Partner across the PDE triad. Your design counterpart will co-own the experience. Your engineers will be your technical architecture collaborators. Your job is to make the product better than any one of you could on your own — and to be the person who makes the decision when consensus is not reachable.

• Work directly with customer teams. Your job will be to test your hypotheses, directly with our customers and partners, and iterate quickly to ensure they can feel the value. You will be in their rooms — sometimes literally — learning what customers actually need and translating it back into the product.

What We Value

• Living by our values over avoiding conflict. We favor transparency and honest debate because strong decisions depend on saying the hard things out loud.

• Transformation over predictability. We dedicate our efforts to work that produces monumental outcomes for our customers, even if it means trading short-term predictability for long-term impact.

• Intensity over comfort. We move with urgency and focus, especially when the work is messy or unstructured.

• Craftsmanship over throughput. We take pride in how things are built. Quality, coherence, and care matter. We would rather ship fewer things done right than many things done carelessly.

• Ownership over process. We hand PMs problem spaces, not tasks. We trust them to drive their domain end to end, bring the right people along, and expand what the team is capable of.

Required Qualifications

• Experience & DNA: 6+ years in product management, ideally with at least 2+ years building AI/ML or LLM-powered products. Startup experience strongly preferred. You have shipped at least one product or major feature from a blank page to real customers, and you can tell that story in detail — including what you would do differently if you ran it back.

• Technical Depth: You have been an engineer at some point in your career, or your product work has been deeply technical enough that engineers respect your architectural instincts. You can read the code, you have opinions on the system design, and you can make the tradeoffs without outsourcing them. Comfortable in technical conversations about API design, system architecture, and prompt engineering. Knowing how to play around with data and draw conclusions is a baseline requirement.

• AI Fluency: Hands-on experience with LLM APIs (OpenAI, Anthropic, or equivalents), embeddings, vector stores, tool use, prompt design, and evaluation. Not just current tool mastery, but persistence, curiosity, and vision. You experiment with AI tools in and outside of work, you do not give up when they fail, and you can articulate where this industry is heading in the next few years and how teams should operate in it.

• Player-Builder Mentality: You do not want just to direct the work; you want to be in it. You prototype. You write. You ship. You dogfood aggressively and dig into the logs to understand why an agent failed. You use modern AI tools aggressively to multiply your own output.

• Opinionated with an Open Mind: You hold strong product convictions and will push back on a Head of Product, a customer, or a head of engineering when you think they are wrong. You also take "you are wrong" with curiosity, not defensiveness or submission.

• Outcome-Driven Mindset: You understand that great products drive business. You will be measured not just on shipping, but on customer satisfaction (CSAT), engagement, and our ability to convert design partners and prospects into successful, closed deals.

• Hacker Spirit: You love solving puzzles. You are happier with a hard, ambiguous problem than a clean, well-scoped one. You poke at edges, ask uncomfortable questions, and would rather find problems yourself than have them find you. You get restless when the work gets too comfortable.

Preferred Qualifications

• Domain experience in developer tools, engineering productivity, dev infrastructure, coding assistants, or agent platforms.

• You have shipped a product at a Series A or Series B startup, not just in a large company.

• You have worked on products where humans and AI agents collaborate on the same work, and you have a point of view on what that should feel like.

• You have seriously experimented with modern AI-native tools (Cursor, Claude Code, Codex, Devin, v0, Replit Agent, or equivalents) and have opinions on where each of them wins and loses.

• You have built and run eval frameworks for an agentic or LLM-powered product in production — and have war stories about what broke when models changed under you.

• CS degree or equivalent engineering background.
  

Other

• Location: San Mateo, CA (Seattle considered in exceptional cases)

• Location Type: Hybrid (with in-office attendance three days a week)