GoodLeap is revolutionizing the sustainable solutions industry through their technology platform that has facilitated over $30 billion in financing since 2018. As a Staff Software Engineer – AI Agents, you'll be at the forefront of developing intelligent, conversational, and task-driven products that empower professionals in the trades industries and homeowners to adopt sustainable solutions. This hands-on technical leadership role combines deep technical expertise with strategic thinking, where you'll architect and deliver production-grade AI agent capabilities while setting long-term technical direction.
The role involves leading the development of sophisticated AI agent platforms, including multi-modal interactions, multi-agent orchestration, and memory systems. You'll work with Python and FastAPI to build robust backend services, integrate with various LLM APIs, and develop vector databases for enhanced agent intelligence. The position offers the unique opportunity to shape GoodLeap's AI strategy while contributing to their mission of making sustainable solutions more accessible and affordable.
Working in a hybrid environment in San Francisco, you'll collaborate with cross-functional teams, mentor other engineers, and drive technical excellence across the organization. The compensation package includes a competitive salary range of $173,003 - $200,000, plus potential bonuses and equity compensation. This is an opportunity to make a lasting impact on both the company's technology ecosystem and the broader mission of promoting sustainable solutions.
The ideal candidate brings 5+ years of software engineering experience, with specific expertise in AI/ML technologies, distributed systems, and technical leadership. You'll need strong Python skills, experience with LLMs, and the ability to balance innovation with practical delivery. This role is perfect for someone who combines technical excellence with strategic thinking and wants to be at the cutting edge of AI development while contributing to environmental sustainability.