Staff Platform Manager, Conversational Products
New
A
AirbnbConversational AI
US - Remote EligibleFull-TimeManager
Salary$200,000 — $240,000 USD
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Job Details
- Experience
- 9+ years building technology products, with at least 5 in product management or a closely related technical role (engineering, data science, or applied research) from which you owned product direction.
- Required Skills
- Machine LearningProduct ManagementGenerative AI
Requirements
- Have 9+ years building technology products.
- Have at least 5 years in product management or a closely related technical role, such as engineering, data science, or applied research, with ownership of product direction.
- Have direct experience shipping generative AI or ML products to production.
- Have experience with retrieval-augmented generation, agentic or function-calling architectures, and evaluation systems; these are identified as ideal experience.
- Demonstrate hands-on technical depth, including reading and debugging prompts and understanding ML and engineering constraints.
- Be comfortable authoring artifacts that shape production behavior.
- Be able to work directly with production data and validate analyses end to end.
- Have designed, calibrated, or certified automated model-based evaluations and translated quality signals into measurable diagnostic components.
- Bring a strong understanding of user experience, including latency, tone, and trust in AI interactions.
- Be able to own an ambiguous cross-team mandate and drive alignment.
- Have a track record of scaling impact through others by setting standards, assigning owners, and holding them accountable.
- Demonstrate strong executive communication and the ability to lead with decisions and asks.
Responsibilities
- Set the product direction and multi-quarter vision for how the assistant reasons and responds.
- Work directly with production data and use AI tools and coding agents to investigate and validate analyses.
- Build assistant capabilities end to end, including hands-on work to make behavior real and calibrate it with human input.
- Define correct and complete resolutions for user problems and align engineering, policy, and knowledge partners.
- Define measurable success criteria for resolution, answer accuracy, safety, and launch readiness.
- Own launch readiness for major model and platform changes, balancing learning speed with safety and risk requirements.
- Diagnose failures in complex AI systems and determine where fixes belong.
- Own evaluation strategy, including LLM-based evaluators, offline and live-traffic evaluation, calibration, certification, and improvement tooling.
- Align cross-functional teams, establish partner agreements, and drive a single source of truth across the product.
- Present decisions, tradeoffs, and asks to leadership, tailoring the narrative to the audience.
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