Lead Software Engineer

AIFund•Santa Monica, CA

16d•$175,000 - $200,000•Hybrid

About The Position

Capsure is an AI-powered compliance platform purpose-built for the clean energy industry. We automate the verification of Prevailing Wage and Apprenticeship (PWA) requirements under the Inflation Reduction Act -- the compliance obligations that determine whether renewable energy developers keep or lose their IRA tax credits. The stakes are real. A single construction project can carry $50M+ in tax credit exposure. Developers, EPC contractors, and their tax equity partners need certainty that compliance holds up to IRS scrutiny in real time, not months after the fact. We built the alternative: an AI system that ingests certified payroll data, validates it against DOL wage determinations and apprenticeship program requirements, surfaces violations in real time, and generates the audit trail needed for tax credit certification. Capsure is backed by AI Fund (Andrew Ng) and AES Clean Energy, one of the largest renewable energy developers in the world, as our anchor design partner and early customer. The product is built, the first customer relationship is deep, and the pipeline is building. Now we need the engineering foundation to match the ambition. You will be Capsure's first engineering hire and the technical backbone of the company. This is not a role where you execute someone else's tickets. You will make architectural decisions, set engineering standards, own the codebase, and build the AI-powered workflows that make our compliance engine genuinely better than anything else in the market. You will work directly alongside our CEO, who is technical and actively involved in product and engineering, and operate with significant autonomy. We move fast using AI coding tools and agents as force multipliers, not crutches. The expectation is that you are already building this way and can push the pace further. As we scale, this role grows with it. You are not just the first engineer -- you are the future engineering leader.

Requirements

Genuinely AI-native engineer: you live in Cursor, Windsurf, Claude Code, or equivalent. AI tooling is how you build, not a novelty.
Strong full-stack capability: React/TypeScript on the frontend, Node/TypeScript or equivalent on the backend, comfortable in SQL and relational data modeling.
Experience building AI agents and automation workflows in production -- not toy demos, but systems with real users and real consequences.
Architectural judgment: you know when to move fast and when to slow down, when to abstract and when to hardcode, when to build and when to buy.
Security-minded by default. Our customers share payroll data, SSNs, and financial records.
Independent operator who needs direction on priorities, not on how to execute.

Nice To Haves

Experience with Supabase, PostgreSQL, and serverless/edge function architectures (Deno a plus).
Demonstrated history of shipping meaningful software at startup pace.
Experience with document processing pipelines: parsing PDFs, Excel, Word documents; working with structured and unstructured data.
Comfort working in a domain you do not initially know deeply. Compliance and regulatory logic is learnable; the engineering fundamentals are not.
Excitement about the clean energy transition.

Responsibilities

Own the Product Engineering
Architect, build, and ship full-stack features across our React/TypeScript frontend and Supabase/PostgreSQL backend.
Write production-quality code while moving at startup speed, using AI coding tools to dramatically compress the time between idea and shipped feature.
Make and defend architectural decisions: data modeling, API design, security, performance, scalability.
Maintain code quality, testing standards, and documentation without being told to.
Build AI Agents and Automation Workflows
Design and implement AI agents and multi-step automation pipelines that power our compliance engine: document ingestion, payroll validation, issue detection, remediation workflows.
Work with LLMs (currently Google Gemini and Claude) to build intelligent extraction, classification, and validation systems.
Build robust, observable, production-grade AI workflows with proper error handling, retry logic, audit trails, and human-in-the-loop checkpoints.
Stay current on the evolving AI engineering landscape and bring in the right tools as they mature.
Be the Engineering Standard
Establish patterns and practices that will scale as we grow the team.
Own security rigorously: data handling (PII, SSNs, payroll data), authentication, authorization, and audit logging are core to our value proposition.
Instrument the system: logging, monitoring, error tracking, performance.
Eventually recruit, mentor, and lead additional engineers as the team grows.