# Senior AI Builder

**Company:** [Earnin](https://hotfix.jobs/companies/earnin)
**Location:** Mountain View, CA
**Role:** ML Engineering
**Salary:** $228k – $279k/yr
**Experience:** 4+ years
**Skills:** Llm Integration, Prompt Engineering, RAG, Agent Architectures, Mcp, LangChain, Open Source Llms, Python, Reinforcement Learning, RLHF, Ai Governance
**Posted:** 2026-07-09

> Senior AI Builder responsible for designing and shipping production AI agents, agentic workflows, evaluation harnesses, and reusable AI tooling that transform EarnIn's product development lifecycle. Requires 4+ years software engineering experience with strong LLM/agent expertise.

## Job Description

## What You'll Do
- Design agents that take real actions: prompts, reasoning chains, tool calls, full architecture including MCP servers, agent scaffolding, context harnesses, and a Skills Marketplace. Build production infrastructure that every squad at EarnIn builds on.
- Rethink the product development lifecycle (PDLC): replace manual friction with agentic workflows in scoping, design, review, testing, deployment, and monitoring. Build evaluation pipelines, automated PR hygiene, deployment gating, and generation-to-merge metrics.
- Own evaluation infrastructure: build pipelines, benchmarks, and quality gates for AI-assisted workflows. Design eval harnesses, set generation-to-merge and review latency baselines, make model quality visible and trustworthy.
- Turn AI pilots into production: build reusable libraries, templates, and reference implementations that squads can fork and ship quickly. Focus on reusable, reliable AI integration patterns.

## What Success Looks Like
- Teams across engineering ship AI-assisted features faster with fewer rework loops.
- Harnesses built are actively used and well-documented.
- AI pilot quality and safety metrics are visible, trustworthy, and improving.

## 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 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 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 track record of building things other engineers actually use.

## Nice-to-Haves
- Hands-on experience with reinforcement learning (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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