Amplitude is the leading AI analytics platform, helping over 4,700 customers—including Atlassian, Burger King, NBCUniversal, and Square—build better products and digital experiences. With powerful AI Agents embedded across our platform, teams can analyze, test, and optimize user experiences faster than ever. Ranked #1 across multiple categories in G2’s Winter 2026 Report, Amplitude is the best-in-class solution for product, data, and marketing teams. Learn more at amplitude.com. As an organization, we deliver for our customers by living our values. We operate from a place of humility, take ownership of problems and successes, approach challenges with a growth mindset, and put our customers at the center of everything we do. Amplitude’s Commitment to Diversity Equity & Inclusion (DEI): Amplitude believes that diversity enables the creation of better products, improves the ability to solve complex problems, and drives more powerful solutions. We strive to create an environment of inclusion—one focused on psychological safety, empathy, and human connection—that will allow employees of all backgrounds to thrive. About The Role & Team Amplitude is building analytics for the agent era. Our Agents can investigate, create signals and surface patterns nobody thought to look for. As our Principal Product Marketing Manager for Agents, you’ll define how we tell that story to the market—from our agent capabilities themselves to the observability layer that helps teams understand if their agents are actually driving results. This is a category-defining role. You’ll shape how developers, product teams, and enterprise buyers think about agents, observability, and evaluation. You’ll partner closely with Product, Engineering, and GTM teams to turn complex systems into narratives that actually land—and evolve them as fast as the market does. If you’d rather define a category than inherit one, you’ll feel right at home. As a Principal Product Marketing Manager, you will: • Define and own Amplitude’s point of view on agent analytics—what “good” looks like, how teams should evaluate agents, and why behavioral data is critical to improving real-world outcomes (not just model benchmarks) • Turn deeply technical concepts into market-shaping narratives—connecting agent architecture (LLM evals, context engineering, tracing, orchestration) to business impact • Lead 0→1 launches for agent capabilities—building positioning, demos, and technical deep-dives that reflect how teams actually build today using tools like Cursor, Claude Code, LangChain, and internal eval frameworks • Create proof that moves skeptical, technical buyers— Work with customers to establish best practices and learnings that drive adoption. Turn customer feedback from AI builders, startups, and enterprise teams into stronger signals for the product roadmap. Build the case studies and best practices that drive adoption. …
Aggregated by Frontier · Posted April 27, 2026