Founding AI Engineer

Daylight Infrastructure•San Francisco, CA

19h•Onsite

About The Position

Daylight Infrastructure is a market infrastructure company accelerating the commercialization of clean energy and industrial technologies. We help scale solutions in clean firm power, fuels, industrial heat, cement, and steel—sectors responsible for over one-third of global emissions. We're building an AI-native platform that connects energy suppliers with hyperscalers and data center developers. Our tools automate site screening, diligence, and help both sides move from evaluation to signed agreements faster. Our first vertical focuses on procuring clean, reliable power for data centers. Speed to power is the bottleneck for intelligence. Our platform will expand to other critical infrastructure and commodities over time. We believe the bottleneck in scaling clean energy infrastructure isn't the tech. We are solving the capital flows, coordination, and contracting to power intelligence faster and cleaner. Florence AI will eventually be the underlying intelligence for energy projects, industrial facilities, and other physical infrastructure. We’re hiring a founding AI engineer to build the core intelligence layer of our product. This is a true founding role: you will take high ownership and exercise real technical leadership. You will shape Daylight’s technical direction, set the standards for how we build, and help lay the foundations for a world-class engineering organization. You’ll work directly with the founder to automate structured contracting, extract insights from messy data and compliance documents, and support smarter purchasing decisions. We’re seeking strong GenAI/agent systems experience (LLM orchestration, retrieval, tool use) and solid backend skills (Python/TypeScript, APIs, data pipelines, security). You will translate energy procurement workflows into usable software, prototype quickly, and ship to production. You work autonomously, prioritize ruthlessly, set clear milestones, and bring a calm, analytical, low-ego presence. Ready for co-founder-level responsibility and startup volatility.

Requirements

Strong GenAI/agent systems experience (LLM orchestration, retrieval, tool use)
Solid backend skills (Python/TypeScript, APIs, data pipelines, security)
Familiarity with AI engineering, agents, harnesses, vector DBs, and orchestration frameworks including LangChain or LlamaIndex
Strong engineering skills and can take projects end-to-end, making good technical tradeoffs throughout
Shipped products to real users and have experience working directly with customers to translate workflows into product
Adaptable and embrace ambiguity, pivoting as needed
Experience setting direction, and providing leadership and mentorship as the team grows
Deeply motivated by climate impact and energized by the challenge of building from zero to one.
Wildly driven and curious, and excited to throw yourself into the fire of an early-stage startup.
Deep, niche intellectual obsessions.
Exhibit high agency and take ownership end-to-end.
Highly collaborative and bring a low-ego, team-first approach.
Bring compassion and deep care for the people you work with, and help create a supportive, high-trust community.
Real comfort with ambiguity, and can create structure where none exists.

Nice To Haves

Power markets knowledge
AWS/GCP experience
Unit-economics modeling
Experience with contracts, procurement systems, or trading
Enjoys team hikes and bike rides :)

Responsibilities

Own technical strategy and execution: Set architecture and technical plan (data model, AI systems, infra, security), write production code, and set the quality bar (tests, reviews, observability).
Build AI agents: Design and ship agentic workflows that enable top datacenter developers to contract, diligence, and close 10 GW sites, and $500 million contracts faster than ever before
Build software features end-to-end: Turn procurement and project finance workflows into usable product; be forward-deployed with users and iterate in real time.
Build data engineering + geospatial pipelines: Ingest and enrich datasets (EPA GHGRP, EIA, NREL, parcels, utility filings) capturing grid access, substations, transmission, and land availability.
Drive PMF through engineering: Work closely with the founder to find PMF through user conversations and rapid iteration.