• Develop a bespoke performance management framework tailored for a fast-paced AI startup (including performance reviews, 360-degree feedback, and competency mapping). • Implement AI-driven tools to analyze sentiment in feedback and identify patterns of high performance or potential burnout. • Move away from annual reviews to a "Continuous Performance Management" model with real-time feedback and regular check-ins. • Use AI to map current team skills against industry benchmarks and predict future talent needs. • Establish leading indicators of performance using data from Slack, Jira, or CRM rather than just lagging indicators. • Ensure the new system seamlessly integrates with our existing OKR framework. • Conduct workshops and create guides for leads on how to give/receive feedback and conduct meaningful 1-on-1s.

• Develop a bespoke performance management framework tailored for a fast-paced AI startup (including performance reviews, 360-degree feedback, and competency mapping). • Implement AI-driven tools to analyze sentiment in feedback and identify patterns of high performance or potential burnout. • Move away from annual reviews to a "Continuous Performance Management" model with real-time feedback and regular check-ins. • Use AI to map current team skills against industry benchmarks and predict future talent needs. • Establish leading indicators of performance using data from Slack, Jira, or CRM rather than just lagging indicators. • Ensure the new system seamlessly integrates with our existing OKR framework. • Conduct workshops and create guides for leads on how to give/receive feedback and conduct meaningful 1-on-1s.

• Drive implementation of Plus Power’s AI operating model by building organizational capability, supporting adoption, and enabling responsible use of AI across the business. • Build organizational capability for AI through training, guidance, reusable patterns, and practical support that helps teams apply AI effectively in day-to-day work. • Define and reinforce AI standards, guardrails, and usage patterns to promote consistency, quality, trust, and responsible adoption across teams. • Partner with leaders and cross-functional stakeholders to assess, prioritize, and implement AI opportunities with meaningful business impact and strong operational fit. • Design, develop, and support AI-enabled solutions and workflows, including copilots, agents, prompt-based workflows, and human-in-the-loop processes, with attention to practicality, governance, and execution quality. • Facilitate the transition from experimentation to scale by identifying high-value use cases, standardizing proven approaches, and embedding AI solutions into durable business processes and platforms. • Contribute to BO&SA’s broader analytics, reporting, and operational intelligence priorities, while helping measure AI adoption, solution effectiveness, and business impact over time.

• Contribute towards the long-term vision for building 10-star, personalized community support, building a platform where our AI understands each guest and host the way their best human advocate would, and responds accordingly. • Partner with data science, ML, and operations to identify new high-signal personalization signals to serve user needs • Own the product roadmap across ML models, signals, schemas, and prompt optimization to ensure the right information reaches the model in the most effective form. • Drive personalization experiment design and measurement: define KPIs (self-solve rate, personalization coverage, citation accuracy), lead experiment design, and translate results into clear product decisions. • Collaborate with ML engineers to shape requirements for accuracy, latency, and scalability improvements. • Scale personalization infrastructure across Airbnb's AI surfaces, ensuring the platform is extensible enough to serve each modality's unique needs. • Build a framework for continuous improvement: define how the team identifies personalization failures, prioritizes fixes, and systematically closes the gap between AI and human agent performance. • Collaborate across engineering, ML, design, data science, and policy to build consensus on prioritization, drive cross-functional alignment, and ship with rigor from concept to production.