Director, Paid & Digital

About the role

We are an applied AI lab building end-to-end software agents. We're the makers of Devin, the first AI software engineer. Our team is extremely talent-dense. Among our founding team, we have world-class competitive programmers, former founders, and leaders from companies at the cutting edge of AI including Scale AI, Palantir, Cursor, Waymo, Tesla, Lunchclub, Modal, Google DeepMind, and Nuro. Building Devin is just the first step—our hardest challenges still lie ahead. If you’re excited to solve some of the world’s biggest problems and build AI that can reason on real-world tasks, apply to join us.

Responsibilities

• Build Cognition's Paid Demand Engine – own paid acquisition and distribution strategy focused on generating qualified enterprise pipeline; determine channel mix, investment strategy, audiences, offers, and campaign architecture; understand where highest-value audiences spend time; allocate budget dynamically based on performance; build systems, processes, and measurement framework to scale paid investment; partner with sales, operations, and sales enablement to ensure leads are qualified and effectively worked.
• Own Paid Media – own strategy and execution across paid search, LinkedIn, YouTube, Reddit, Meta, X, and other platforms; build sophisticated audience and account strategies around enterprise segments, personas, intent, and buying stage; capture high-intent demand and create demand among accounts not actively searching; continuously optimize campaigns based on pipeline quality and downstream business outcomes; maintain deep platform expertise and stay close to execution to spot opportunities.
• Expand Paid Distribution Beyond Traditional Advertising – identify and test new channels where technical and enterprise audiences spend attention; build partnerships across newsletters, technical publications, developer and enterprise communities, podcasts, creators, influencers, and emerging distribution channels; evaluate opportunities based on audience quality, enterprise relevance, scale potential, economics, and testability; build repeatable process for finding, testing, measuring, and scaling new sources of demand; continuously challenge where Cognition should spend its next dollar.
• Build a High-Velocity Experimentation Engine – run continuous pipeline of experiments across channel × audience × message × creative × offer × format × landing experience; develop clear hypotheses and success criteria before tests; test fundamentally different approaches; quickly scale what works and shut down what doesn't; build culture where experimentation velocity increases as program grows; track investment impact throughout sales journey; turn campaign learnings into insights that improve broader Demand Gen strategy.
• Build the Creative Testing System – develop systematic approach to paid creative experimentation; identify which messages, narratives, customer proof, product capabilities, and formats resonate with different audiences; partner with Product Marketing and Design to maintain pipeline of new creative concepts; test across video, static, product-led creative, customer proof, technical content, executive perspectives, and emerging formats; use performance data to inform next generation of creative.
• Develop Offers That Create Demand – partner with Web/CRO, Product Marketing, and Demand Gen team to determine what paid distribution should be behind; test different offers across stages of enterprise buying journey (product experiences, technical content, customer proof, benchmarks, assessments, executive content, direct sales engagement); identify when existing offer isn't working and help develop better; match right offer to right audience.
• Optimize for Enterprise Pipeline – measure success based on qualified enterprise pipeline and downstream business impact; partner with RevOps to understand performance from impression → account engagement → opportunity → pipeline; understand performance by audience, account segment, channel, campaign, offer, and creative; develop clear frameworks for CAC, pipeline efficiency, marginal spend, and incrementality; use data to determine where to increase, decrease, or reallocate investment.
• Work AI-Natively –