Little more about the team:

You'll be the product manager for our Canvas and Agentic AI teams - the teams building Honeycomb's AI-powered investigation experience. Canvas lets engineers use natural language to query, visualize, and understand their production systems. We're pushing well past chat: we're building Skills (pre-configured workflows that encode domain expertise into repeatable, executable actions), Auto-Investigations (agent-driven investigations that kick off before a human gets paged), and a rearchitected Canvas that's both interactive and collaborative.

You'll work in a tight triad with engineering and design leads, owning the roadmap for how AI shows up in Honeycomb, from the product UI to our MCP server. You'll partner with our Director of AI Strategy, Research & Development teams, and our customer-facing teams to stay grounded in what customers actually need while pushing toward something meaningfully different from what exists today.

This is a role for someone who can hold both execution and ambiguity: shipping production features while navigating a product space where the right answers are still emerging.

What you’ll do in the role:

Define AI product strategy in service of Honeycomb's vision

• Synthesize AI trends, customer needs, and technical capabilities into clear strategic recommendations
• Help the product organization understand where and how AI creates genuine customer value vs. where it's hype
• Identify emerging observability challenges created by AI adoption (e.g., LLM drift, prompt regression, low-context generated code)
• Make informed bets about which AI technologies and approaches warrant investment

Conduct research that bridges technology and customer reality

• Build deep understanding of how customers are adopting AI in their engineering workflows
• Identify patterns across customer conversations that signal future market needs
• Stay current with AI/ML developments (RAG, fine-tuning, agentic systems, etc.) and translate implications for observability
• Engage with technical communities, researchers, and early adopters to spot trends before they go mainstream

Enable AI innovation across the product portfolio

• Partner with product teams to identify high-value opportunities to leverage AI capabilities
• Provide guidance on AI approaches, trade-offs, and best practices without centralizing all decisions
• Help teams understand how their work fits into our broader AI strategy and how to leverage shared capabilities
• Synthesize cross-portfolio learnings and socialize what's working (and what isn't)

Evangelize the AI-observability connection

• • Articulate Honeycomb's point of view on AI observability both internally and externally
  • Help customers understand how observability needs evolve as they adopt AI
  • Contribute to thought leadership through writing, speaking, and community engagement
  • Build credibility with technical audiences who care deeply about how we use AI, not just that we use it

What you’ll bring to the role:

Proven AI product experience

• 5+ years in product management with at least 1+ year shipping AI/ML-powered products
• Track record of taking AI features from concept to customer impact, including things that didn't work
• Deep understanding of how engineering teams actually build, deploy, and operate AI systems (not just theoretical knowledge)
• Familiarity with current AI/ML techniques and the judgment to know when they're applicable vs. overhyped

Strategic thinking with customer empathy

• Ability to synthesize disparate signals (research, customer feedback, technical trends) into coherent strategy
• Comfort operating with ambiguity and making decisions without perfect information
• Pattern recognition across conversations—spotting what customers aren't yet saying clearly
• Strong judgment about when to say "no" or "not yet" despite exciting technology

Technical credibility without needing to be the expert

• Enough technical depth to have earned respect from engineers and architects
• Genuine curiosity about how things work, paired with humility about what you don't know
• Ability to evaluate trade-offs between different AI approaches (RAG vs. fine-tuning, model selection, etc.)
• Understanding of observability principles and why they matter in production systems

Communication and collaboration skills

• Excellent written and verbal communication—can explain complex ideas clearly to varied audiences
• Ability to influence without authority across product, engineering, and go-to-market teams
• Comfort presenting to customers, executives, and technical audiences
• Collaborative mindset that elevates others' work rather than centralizing control

Curiosity and learning orientation

• Demonstrated ability to stay current in fast-moving domains (AI is evolving weekly, not yearly)
• Intellectual humility—you're more interested in learning than being right
• Excitement about the intersection of AI technology and real customer problems
• Energized rather than exhausted by the pace of change in AI