Founding Engineer - AI & Full-Stack

JeffreyM Consulting•San Francisco, CA

1d•Onsite

About The Position

We’re working with an early-stage AI startup building intelligent software that automates complex, high-volume customer workflows across voice, SMS, and email. This is a true founding-level engineering opportunity for someone who wants significant ownership, moves quickly, and enjoys solving problems where there isn’t an established playbook. You’ll work across the full stack while helping build and scale production AI agents, integrations, and workflow systems.

Requirements

1–6 years of software engineering experience
Strong TypeScript and Python experience
Full-stack development experience
Experience shipping real production software
Strong software engineering and computer science fundamentals
Experience owning projects or systems end-to-end
Ability to make sound technical decisions with limited direction
Comfortable working through ambiguous, technically challenging problems
Strong understanding of the systems you’ve built and the architectural decisions behind them
Demonstrated ability to learn quickly and take on new technologies
Willingness to work in-person in San Francisco 6 days/week
Comfortable working in a highly demanding, fast-paced startup environment

Nice To Haves

AI agents or production LLM applications
LLM orchestration
Voice AI
SMS/email automation
Conversational AI
Agent state and memory
Complex API integrations
Reverse-engineering or integrating with legacy systems
Scheduling or workflow systems
Healthcare or other highly regulated industries
High-volume communication platforms
AWS
Next.js
TypeScript
Python
Open-source or significant personal projects
Early-stage or founding startup experience

Responsibilities

Build and scale production AI agents across voice, SMS, and email
Design systems that maintain context and consistency across multi-channel interactions
Develop full-stack product experiences and internal tools
Build complex API integrations and custom adapters
Work with external systems that may have limited or inconsistent APIs
Develop scheduling and workflow orchestration systems
Build infrastructure for scaling LLM-powered applications
Improve agent state, memory, personalization, and reasoning
Own features from problem definition → design → implementation → deployment → iteration
Troubleshoot production issues and make architectural decisions as the product evolves
Work closely with the founding team to turn ambiguous problems into working products