Hiring AI Product Engineers: The Complete Guide

# AI Product Engineer

**Location:** San Francisco, CA (Hybrid) · **Employment Type:** Full-time · **Level:** Mid-Senior

## About [Company]

[Company] is building AI-powered tools that help 500,000+ professionals work smarter. Our AI features—writing assistance, research, analysis, and automation—save users hours every week.

We're at the forefront of the AI product revolution. We ship fast, learn from users, and iterate. Our AI features aren't demos—they're production systems that hundreds of thousands of people depend on daily.

**Why join [Company]?**

- Build AI products used by 500K+ users daily
- Ship fast in a rapidly evolving space
- Work across the full stack (prompts to pixels)
- Small team with outsized impact
- Series B company, well-funded, growing fast
- Competitive compensation with meaningful equity

## The Role

We're looking for an AI Product Engineer to help us build the next generation of AI-powered features. You'll work across the entire stack—from prompt engineering and LLM integration to frontend UX and production infrastructure.

This is product engineering for the AI era. You need to understand what LLMs can and can't do, design user experiences that handle AI uncertainty gracefully, and ship features that users actually love (not just impressive demos).

The ideal candidate has shipped AI features to production and understands the gap between "cool demo" and "reliable product." You're excited about the possibilities but grounded in the realities of production AI.

## What You'll Work On

### First 30 Days
- Onboard to our AI stack, prompt library, and product surfaces
- Understand our users, their workflows, and how they use AI features
- Ship your first AI feature or improvement

### First 90 Days
- Own an AI product surface (writing, research, or analysis)
- Build new AI features from conception to production
- Develop evaluation frameworks for your features
- Collaborate with design on AI user experience

### First Year
- Lead AI product initiatives with significant user impact
- Architect improvements to our AI infrastructure
- Develop new AI capabilities as the technology evolves
- Shape our AI product strategy

## Objectives of This Role

- Ship AI features that users love and use daily
- Build evaluation systems that ensure AI quality
- Reduce AI latency and improve response quality
- Implement safety guardrails that protect users
- Scale AI infrastructure for growing usage

## Responsibilities

**AI Feature Development (50%)**
- Design and build AI-powered features end-to-end
- Develop effective prompts and prompt chains
- Implement RAG systems for context-aware AI
- Build streaming interfaces for real-time AI responses
- Create AI features that feel fast and responsive

**Product & UX (30%)**
- Design user experiences that handle AI uncertainty
- Build interfaces for AI editing and refinement
- Create fallback experiences when AI fails
- Implement user feedback loops for AI improvement
- Collaborate with design on AI interaction patterns

**Quality & Infrastructure (20%)**
- Build evaluation frameworks for AI output quality
- Implement safety guardrails and content filtering
- Monitor AI usage patterns and costs
- Optimize for latency and reliability
- Create logging and debugging tools for AI pipelines

## Required Skills and Qualifications

**Technical Skills**
- 5+ years of product engineering experience
- 2+ years building with LLMs/AI in production
- Full-stack development skills (frontend and backend)
- Strong prompt engineering experience
- Production LLM integration experience (OpenAI, Anthropic, etc.)
- Proficiency in TypeScript/JavaScript and Python

**Product Skills**
- Strong product intuition and user empathy
- Experience shipping AI features to real users
- Understanding of AI limitations and failure modes
- Ability to design for AI uncertainty

**Soft Skills**
- Fast iteration and shipping velocity
- Clear communication with product and design
- Comfort with ambiguity in a fast-moving space

## Preferred Skills and Qualifications

- RAG system implementation experience
- AI agent and tool use development
- Fine-tuning and model customization experience
- AI safety and alignment awareness
- ML/AI background (helpful but not required)
- Published AI products or open-source contributions

## Tech Stack

**Frontend:** React, TypeScript, Next.js

**AI:** OpenAI API, Anthropic Claude, LangChain, Vercel AI SDK

**Backend:** Python (FastAPI), Node.js

**Vector:** Pinecone, pgvector for RAG

**Infrastructure:** Vercel, AWS, custom streaming infrastructure

**Evaluation:** Custom eval frameworks, user feedback systems

## Compensation and Benefits

**Salary:** $170,000 - $230,000 (based on experience)

**Equity:** 0.08% - 0.25% (4-year vest, 1-year cliff)—early team, meaningful equity

**Benefits:**

*Health & Wellness*
- Medical, dental, and vision insurance (100% covered)
- $100/month wellness stipend
- Mental health support through Lyra

*Time Off*
- Unlimited PTO with 15-day minimum encouraged
- 11 paid company holidays
- 16 weeks paid parental leave

*Professional Development*
- $3,000 annual learning budget
- AI/ML conference attendance (NeurIPS, AI Engineer Summit)
- Access to latest models and AI tooling

*Financial*
- 401(k) with 4% company match
- Equity refresh grants as company grows

*Workspace*
- $2,000 home office setup allowance
- Hybrid work (flexible, optional SF office)
- Latest MacBook Pro, equipment of choice

## Engineering Culture

**How we work:**
- Ship fast, learn from users, iterate
- AI-first product thinking
- Quality matters—AI must be reliable, not just impressive
- Safety and user trust are non-negotiable
- Small team, high ownership

**What we value:**
- Shipping over perfection (but AI must work)
- User feedback driving product direction
- Honesty about AI limitations
- Moving fast as the technology evolves
- Work-life balance (sustainable pace for the long run)

## Interview Process

Our interview process is fast—typically 1-2 weeks.

- **Step 1: Recruiter Screen** (30 min)
  Background discussion and role overview.

- **Step 2: Technical Screen** (60 min)
  Full-stack and AI fundamentals.

- **Step 3: AI Product Deep Dive** (90 min)
  Build or design an AI feature together. Show us how you think.

- **Step 4: Product & Design** (60 min)
  Discussion about AI UX, user experience, and product thinking.

- **Step 5: Founder Chat** (45 min)
  Culture, vision, and mutual fit.

We move fast. If there's mutual interest, we can complete the process in a week.

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*[Company] is an equal opportunity employer.*