Moloco
Staff Software Engineer - AI Applications
$187K–$275K
Menlo Park, California, United States +2 · Staff · Full-time · Posted Oct 20, 2026
- Location
- Menlo Park, California, United States
Seattle, Washington, United States
Menlo Park, California, United States - Experience
- 8+ years of professional software engineering experience.
About the job
Build and operate generative-AI products and tools that improve Moloco’s internal workflows and customer experiences, owning them end-to-end from problem identification through launch.
What you'll do
- Develop production-scale LLM applications and AI-powered products, including agentic systems, RAG, and tool-using assistants.
- Design shared technical foundations such as evaluation and monitoring systems, APIs, standards, and guardrails for teams to build on autonomously.
- Identify high-impact problems, shape technical direction with stakeholders, and lead product-area engineering strategy.
What they ask for
- 8+ years of professional software engineering experience, including ownership of complex, cross-functional systems end-to-end.
- Deep hands-on experience architecting production-scale LLM applications, including tradeoffs among latency, cost, reliability, and quality.
- Experience with agent frameworks or orchestration and multi-step reasoning systems; proven experience designing AI product evaluation and monitoring systems.
- Strong systems design, fluency across frontend, backend, and infrastructure, and experience influencing technical direction beyond one’s team.
Nice to have
- Experience building internal AI platform capabilities or leading AI-first SaaS rationalization/build-versus-buy efforts.
- Experience redesigning critical business workflows around AI; platform or infrastructure-adjacent experience with strong product instincts is also valued.
Pay
$220,000 — $275,000 USD base salary; $198,000 — $247,500 USD base salary; $187,200 — $234,000 USD base salary
Tools
LLM, RAG, LangGraph
AI focus
Build production-scale generative-AI applications and internal AI tools; redesign workflows around AI and develop shared capabilities including guardrails, evaluation, and monitoring.
Model-written summary · the original posting is authoritative