AI Builder

EarnIn is making AI-native engineering a core capability — not an initiative, but how we design, build, and ship. We're hiring an AI builder who also writes great software — someone who looks at every step of how we design, build, test, and ship, and asks: why isn't an agent doing this? This is not a side project. The agents and harnesses you build here will run in production, making real decisions for real people — people who depend on EarnIn to access their pay when it matters most. If that bar excites you rather than intimidates you, keep reading. You'll work at the intersection of platform engineering, developer experience, and applied AI — partnering with architects, domain leads, and product engineers to build the tools, patterns, and guardrails that make AI adoption fast, safe, and durable.

Responsibilities

Agents that take real actions — design how agents think: prompts, reasoning chains, tool calls, and the full architecture beneath them; A PDLC that doesn't look like 2022 — challenge each stage from scoping and design through to review, testing, deployment, and monitoring, and replace manual friction with agentic workflows; Evals that actually mean something — own the evaluation infrastructure: building pipelines, benchmarks, and quality gates; Pilots that graduate to production — take what's working and turn it into something the next team can fork and ship in a week, building reusable libraries, templates, and reference implementations

Requirements

3+ years of full-time software engineering experience, with at least 2 years building tooling, platforms, or internal developer products; Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, or a related technical discipline, or equivalent industry experience. We care about what you’ve built, not where you studied.; Hands-on experience with LLM integration patterns — prompt engineering, RAG pipelines, tool/function calling, and agent architectures; Proficiency and comfort working across the stack when needed; Experience with MCP, LangChain, or comparable orchestration frameworks; Experience with open source LLM models; Strong opinions about developer experience and a track record of building things other engineers actually use

Nice to have

Hands-on experience with reinforcement learning — especially RLHF, RLAIF, or reward modeling in applied product contexts; Experience in fintech or regulated/security-sensitive environments; Hands-on work with AI governance — bias evaluation, audit logging, model cards; Exposure to multi-step reasoning pipelines or human-in-the-loop system design

Benefits

equity; benefits