POSITION SUMMARYEarnIn 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. The Mountain View base salary range for this full-time position is $228,000 - $279,000, plus equity and benefits. Our salary ranges are determined by role, level, and location. This is a hybrid position in Mountain View that requires in-office work 2 days a week.
WHAT YOU'LL DOAgents 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
WHAT WE'RE LOOKING FOR- 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
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