Staff Product Manager

Honeycomb is hiring a Staff Product Manager to shape its AI-powered investigation experience and broader AI product strategy. The role combines customer and market research, cross-portfolio AI guidance, and communication of Honeycomb’s perspective on AI observability.

Responsibilities:

• 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
• 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
• 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)
• • 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

Requirements:

• 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
• 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
• 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
• 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
• 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
• Please note we cannot currently sponsor or support visa transfers at this time