Full Stack Engineer, Applied AI

reval • San Francisco Bay Area
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AI Summary

Build AI systems that reason through operational context, dispatch work to human operators, detect risk, and take action in the real world. Create full-stack AI product features end to end across React, TypeScript, backend services, database schema, agent logic, and evals. Partner directly with design and operations to decide what should be automated, what should be assisted, and what should stay human for now.

Key Highlights
Build AI systems that reason through operational context
Create full-stack AI product features end to end
Partner directly with design and operations
Key Responsibilities
Build full-stack AI product features end to end
Create agents that receive customer requests and dispatch operational work to the right human operators
Design agent loops that can plan multi-step actions
Technical Skills Required
TypeScript React Node Postgres Redis LLM APIs prompting retrieval tool use structured outputs evaluation patterns
Benefits & Perks
Visa sponsorship
Hybrid work arrangement
Salary: $200,000 - $250,000 per year
Nice to Have
OpenAI
Anthropic
custom agent loops
RAG over operational data
in-house evals
Azure
Cursor
Claude Code
AI-assisted development workflows

Job Description


This is a role posted by Reval Recruiting on behalf of a client


Full Stack Engineer, Applied AI

Location: San Francisco

Workplace: Hybrid

Employment Type: Full-time

Visa Sponsorship: This role offers visa sponsorship.


This role is posted on behalf of the following client: A fast-growing applied AI and operations technology startup helping companies execute physical work across the U.S. without building local teams, leasing warehouses, or managing every on-the-ground detail directly. Its platform coordinates real-world work across 50+ U.S. metros, covering more than 70% of the U.S. population, and grew from zero to multi-millions in gross revenue in 2025.


Role Overview

This client is hiring a Full Stack Engineer, Applied AI to help build the intelligence layer behind a platform that turns customer intent into operational execution. This role focuses on productizing frontier models into reliable production systems, including agent loops, retrieval pipelines, internal tools, evals, observability, and full-stack product workflows. This is not an ML research role or foundation model training role; it is a hands-on engineering position building AI systems that reason through operational context, dispatch work to human operators, detect risk, and take action in the real world.


What You'll Do

  • Build full-stack AI product features end to end across React, TypeScript, backend services, database schema, agent logic, and evals.
  • Create agents that receive customer requests and dispatch operational work to the right human operators.
  • Build systems that proactively surface operational risks and take action to mitigate them before they become customer-facing issues.
  • Design agent loops that can plan multi-step actions, call internal tools, ask for help when needed, and recover when reality changes.
  • Build retrieval and structured context systems that ground agents in operational data.
  • Create evals, monitoring, and production observability to measure agent quality and catch regressions before users do.
  • Improve prompts, tool definitions, model choices, and agent architecture based on real production telemetry.
  • Partner directly with design and operations to decide what should be automated, what should be assisted, and what should stay human for now.
  • Help define shared AI engineering patterns that future products can build on.


Who You Are

  • 2+ years of full-stack engineering experience, with strong product engineering instincts and the ability to own work from UI to backend service to database schema.
  • Strong TypeScript experience across frontend and backend, ideally with React, Node, Postgres, and Redis.
  • Hands-on experience shipping AI-driven product features in production.
  • Working knowledge of LLM APIs, prompting, retrieval, tool use, structured outputs, and evaluation patterns.
  • Strong judgment around what should be automated, what should be assisted, and what should remain human-in-the-loop.
  • Comfort with messy real-world workflows, noisy inputs, incomplete data, and systems where AI coordinates with human operators.
  • Bias toward shipping, learning from production, and iterating quickly.
  • Comfort working in an early-stage environment with ambiguity, autonomy, and limited process.
  • Interest in building with tools such as OpenAI, Anthropic, custom agent loops, RAG over operational data, in-house evals, Azure, Cursor, Claude Code, and AI-assisted development workflows.


Compensation

  • Salary: $200,000 - $250,000 per year

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