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Salary: USD 297,000 - 350,000 / annual
Asana is seeking a Senior Staff Engineer to lead technical direction across its AI organization. This role sits at the intersection of AI platform infrastructure, agent systems, and product innovation, spanning multiple engineering teams across San Francisco, New York, Vancouver, and Warsaw.
You will define the technical vision and architecture for Asana's AI organization, including the AI platform, agent systems, and Studio, in partnership with engineering and product leadership. Key responsibilities include:
- Leading strategic, cross-team initiatives such as agent runtime and orchestration, context and memory systems grounded in the Work Graph, and cost-efficient LLM inference at enterprise scale
- Partnering with Product, Design, Data Science, Security, and Legal to move ambitious AI products from prototype to reliable, trusted enterprise features
- Setting the bar for AI quality measurement through offline and online evaluation, production monitoring, and feedback loops that improve agent performance
- Making and guiding high-stakes technical decisions on model and vendor strategy, build-versus-buy tradeoffs, and shared platform abstractions
- Influencing R&D-wide AI strategy by bringing a grounded perspective on the model and agent landscape to senior leadership
- Mentoring staff and senior engineers, growing tech leads across the AI organization, and raising the bar for design and architecture reviews
- Representing Asana's AI engineering work externally through talks, writing, and community engagement to attract senior talent
The role is based in San Francisco with an office-centric hybrid schedule: standard in-office days are Monday, Tuesday, and Thursday. Most employees have the option to work from home on Wednesdays, with Friday flexibility depending on work type and team partnerships.
REQUIREMENTS:
- 10+ years of experience building production software, including significant time as a technical lead whose work spanned multiple teams
- 3+ years building production systems with LLMs or machine learning (agents, retrieval, evaluation, or inference infrastructure)
- Track record of leading architecture and initiatives across organizational boundaries, with decisions that proved right over time
- Hands-on fluency with the current AI landscape, including foundation models, agent frameworks, tool use, and evaluation methods, with strong opinions flexible enough to change with new evidence
- Deep experience with distributed systems and the reliability, latency, and cost challenges of running them at scale
- Ability to communicate clearly at every level, from design reviews with engineers to strategy discussions with executives, bridging product, business, and technical language
- Passion for coaching and mentoring engineers, including senior and staff-level engineers
- Deep care for collaboration and ability to foster an inclusive culture
- Demonstrates curiosity about AI tools and emerging technologies
Plus factors: experience with multi-tenant enterprise SaaS, and with security, privacy, and data governance for AI systems.
