Director of Engineering, AI Platform

<p>We are looking for a Director of Engineering to lead our AI Platform organization. This group builds the foundational systems powering every AI experience across Asana. In this role, you will lead four key teams through their engineering managers: Context (search, retrieval, and knowledge extraction across the Asana Work Graph), LLM Foundations (model serving, inference infrastructure, provider strategy, and evaluation systems), and AI Efficiency (our center of excellence for cost, quality, and performance standards across all AI workloads). Collaborating with engineering managers and senior technical leaders, you will drive the end-to-end strategy, execution, and architecture that define how humans and AI work together at Asana to build trusted, reliable, high-value product workflows for enterprise customers worldwide. Your mission is to make Asana’s AI platform the most reliable, economical, and performant foundation in the industry for agentic enterprise software, giving Asana the leverage to ship AI products faster than anyone else.</p> <p>This role is based in our San Francisco office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements.</p> <h3><strong>What you’ll achieve</strong></h3> <ul> <li><strong>Drive Strategy &amp; Execution Across the AI Teammates Pillar:</strong> Lead the multi-year vision and technical strategy for Asana’s AI platform, covering retrieval and agent context, model serving and inference, model portfolio strategy, and evaluation systems.</li> <li><strong>Optimize AI Infrastructure Costs: </strong>Own cost-per-execution as a primary engineering metric, managing model selection, routing, open-weight versus frontier trade-offs, inference optimization, caching, and prompt efficiency to protect product margins at scale.</li> <li><strong>Establish AI Evaluation Frameworks: </strong>Establish evaluation frameworks, regression prevention, and performance standards that product teams can rely on out of the box, making quality, cost, and latency measurable defaults.</li> <li><strong>Lead and Mentor Teams: </strong>Lead and mentor managers and their teams across San Francisco and New York City, building autonomous owners, recruiting top talent, and maintaining high execution standards.</li> <li><strong>Manage Model Strategy: </strong>Evaluate when to adopt frontier models versus open-weight models and structure provider relationships to prevent vendor lock-in.</li> <li><strong>Architectural Leadership: </strong>Pressure