Earnin Senior AI Builder at EarnIn responsible for designing and building AI agents and infrastructure that operate in production. Focuses on integrating AI into the product development lifecycle and evaluation pipelines.
Responsibilities
Agents that take real actions. You'll design how agents think — prompts, reasoning chains, tool calls, and the full architecture beneath them. From MCP servers and agent scaffolding to context harnesses and a Skills Marketplace, you're building the layer every squad at EarnIn builds on. Not a proof of concept. Production infrastructure.
A PDLC that doesn't look like 2022. The product development lifecycle is overdue for a rethink. You'll challenge each stage — from scoping and design through to review, testing, deployment, and monitoring — and replace manual friction with agentic workflows wherever it makes sense. Evaluation pipelines, automated PR hygiene, deployment gating, generation-to-merge metrics: you'll build the scaffolding that makes governed AI fast, not slow.
Evals that actually mean something. You'll own the evaluation infrastructure — building the pipelines, benchmarks, and quality gates that tell us whether our AI is working, degrading, or ready to ship. That means designing eval harnesses for AI-assisted workflows, setting generation-to-merge and review latency baselines, and making model quality visible and trustworthy across teams. If you've built evals that caught real problems before they hit production, you'll fit right in.
Pilots that graduate to production. EarnIn's engineering leads are running experiments. You'll be the person who takes what's working and turns it into something the next team can fork and ship in a week. Less "interesting prototype," more "thing we rely on." You’ll build reusable libraries, templates, and reference implementations that give squads a running start on AI integration, so no one has to solve the same problem twice
WHAT SUCCESS LOOKS LIKE
Teams across engineering can ship AI-assisted features faster, with fewer rework loops
The harnesses you build are actively used and well-documented
AI pilot quality and safety metrics are visible, trustworthy, and improving
Qualification
Bachelor’sExperience with MCPExperience with open source LLM modelsYOU'LL STAND OUT IF YOU HAVE
Required
4+ 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
YOU'LL STAND OUT IF YOU 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
