The Impact You'll Be Contributing to Moloco:

Our mission is to change how Moloco operates as a company. AI makes it possible to rebuild how work gets done — not to shave minutes off tasks, but to redesign entire workflows so every Molocan operates with the leverage of a much larger team.

Two principles will guide the team. 

• Outcomes first — AI will be a tool, not the goal; teams will pick the best solution for Moloco with the same engineering rigor as any other eng org. 
• Shared foundations, not central control — common infrastructure, guardrails, security, and standards that every team will build on autonomously.

Three core pillars:

1. Enable safely. Safe rails for every Molocan — hosting for AI-developed apps, key management, spend caps, permissions and provisioning. With proper guardrails, neither recent incident would have shipped unreviewed or run unbounded.
2. Rebuild workflows GenAI-first. Redesign the company’s most critical workflows from the ground up around AI — the customer lifecycle from onboarding to campaign management and upsell, the recruiting pipeline from sourcing to offer, quarterly planning and forecasting.
3. Rationalize the stack. Evaluate, consolidate, and selectively replace SaaS where AI-first custom is better — e.g., the BI consolidation and open-weights proposals below.

The Opportunity: 

This role is part of the AI Applications team, an extension of Moloco's generative AI initiative. This team sits higher in the AI stack, closer to the user, and focuses on the delivery layer rather than infrastructure. The primary mission is to build AI-powered products and tools that:

• Make Moloco as a company operate more efficiently internally.
• Improve the internal-to-customer experience using generative AI.
• Improve the end customer experience with AI (lighter focus initially).
• Much of the work is greenfield, with only small parts of the foundation currently in place. Engineers will own products end-to-end, from problem identification through launch and ongoing operations.

How Do I Know if the Role is Right For Me?

• 8+ years of professional software engineering experience, with a track record of owning complex, cross-functional systems end-to-end
• Deep hands-on experience architecting LLM-powered applications at production scale — agentic systems, RAG, or tool-using assistants — including the tradeoffs between latency, cost, reliability, and quality
• Experience with agent frameworks/orchestration (e.g., LangGraph, custom agent loops) and multi-step reasoning systems
• Proven experience designing evaluation and monitoring systems for AI products that others on the team adopt and build on
• Strong systems design skills: able to define architecture and technical standards that multiple teams can build on autonomously (shared infra, guardrails, APIs)
• Fluency across the stack (frontend, backend, infra) with the judgment to know where to go deep vs. delegate
• Experience influencing technical direction beyond your own team — e.g., setting engineering standards, mentoring senior engineers, or leading technical strategy for a product area
• History of independently identifying high-leverage problems (not just executing an assigned backlog) and building conviction with stakeholders and leadership

Preferred Skills:

• Experience building or leading the buildout of internal AI platform capabilities: key management, spend caps, permissioning, hosting for AI-developed apps
• Track record of leading a SaaS rationalization or "build vs. buy" effort involving AI-first custom replacements
• Experience redesigning a critical business workflow (not just automating a task) around AI — e.g., recruiting, customer lifecycle, planning/forecasting
• Prior experience in a platform or infra-adjacent role that also required strong product instincts
• Public or internal thought leadership on agent architectures, evals, or applied LLM engineering practices
• Comfortable operating with minimal process, setting technical direction in a greenfield environment, and shaping team practices as they scale