SR. AI ENGINEER
REVI
San Francisco, CA, US
Hybrid
$220k–$270k · announced
2026-06-18
What this role pays in San Francisco
$113K - $196K
Low
$113K
Median
$149K
High
$196K
Official salary benchmark · BLS OEWS 2025
On the map
map
See this employer on the map — San Francisco
Job description
**Senior AI Engineer @**
**Location:** San Francisco(Hybrid) **Experience:** 8\+ years **Team:** AI Engineering
**About Revi**
Revi is a Series A restaurant commerce platform building the AI operating system for the restaurant industry. We're rapidly transforming how restaurants run their business through agentic AI — from voice agents that handle customer interactions to agentic commerce and intelligent automations across our ReviOS dashboard. We're a small, fast\-moving team where engineers ship to production daily and own meaningful surface area from day one.
**The Role**
We're hiring a Senior AI Engineer to architect, build, and own production\-grade agentic AI systems at Revi. You'll lead the design of single\-agent and multi\-agent systems that take real actions for restaurants and their customers, own the deterministic ML stack that powers personalization and decisioning across the product, and set the bar for how we deploy, monitor, and evolve AI in production. This is an IC role for a builder who has shipped — repeatedly — and knows what production reliability for AI systems actually requires.
**What You'll Do**
* Architect and ship **agentic AI systems** end\-to\-end — designing single\-agent and multi\-agent topologies, tool interfaces, memory and state management, evaluation harnesses, and the production infrastructure that holds it all together
* Own the **deterministic ML modeling** stack — feature pipelines, training, ranking/retrieval, evaluation, and online serving for high\-traffic use cases
* Drive integration with **LLM providers (Claude, GPT, and others)** at production scale — handling latency, cost, reliability, prompt iteration, structured outputs, function/tool calling, and provider failover
* Own **production deployment and operations**: CI/CD, containerization, observability, on\-call quality, cost monitoring, and graceful degradation strategies for AI systems
* Set technical direction and review architectural decisions across the A