Senior AI Builder

EarnIn•Mountain View, CA

4d•Hybrid

About The Position

EarnIn is making AI-native engineering a core capability — not an initiative, but how we design, build, and ship. We're not hiring a software engineer who dabbles in AI. 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.

Requirements

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.
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 Haves

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

Responsibilities

Design how agents think — prompts, reasoning chains, tool calls, and the full architecture beneath them.
Build the layer every squad at EarnIn builds on, including MCP servers, agent scaffolding, context harnesses, and a Skills Marketplace.
Challenge each stage of the product development lifecycle (scoping, design, review, testing, deployment, monitoring) and replace manual friction with agentic workflows.
Build scaffolding for evaluation pipelines, automated PR hygiene, deployment gating, and generation-to-merge metrics to make governed AI fast.
Own the evaluation infrastructure, building pipelines, benchmarks, and quality gates to assess AI performance.
Design eval harnesses for AI-assisted workflows, set generation-to-merge and review latency baselines, and make model quality visible and trustworthy.
Take working AI experiments and turn them into production-ready solutions that other teams can use.
Build reusable libraries, templates, and reference implementations for AI integration.