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# Founding Engineer

- **Company:** Wintermeyer Ventures
- **Location:** New York, NY, United States
- **Salary:** $150,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Databases, Data Infrastructure, Github, Open Source Technology, Virtual Machines, Large Language Models, Backend, Hardware Infrastructure
- **Published:** November 3, 2026
- **Apply:** https://www.careerjet.com/jobad/us57e3dbed204a6f19c67579bc6380d61a

## About the Role

* One to five years of hands-on engineering experience, including startup work or sustained independent or open-source projects.
 * Experience building agentic systems beyond basic LLM SDK integrations, including tool calling, memory, system prompts, and retrieval.
 * Hands-on experience serving, fine-tuning, or evaluating open-source LLMs.
 * Strong full-stack or backend engineering skills, with experience in scalable databases and consumer products.
 * A shipped product or project with measurable adoption, users, or revenue, plus a public GitHub portfolio with recent personal work.
 * Clear communication and the ability to take ownership in a fast-moving, in-person team.
 * Early-stage startup, applied AI, consumer product, open-source, or sustainability experience is a plus.

## Description

Join an early-stage consumer AI team as a founding engineer, building agent systems and product features from architecture through launch. You will help shape a simple, engaging AI experience while improving the infrastructure behind it.
What You'll Do

 * Build and improve the agent harness, including tool use, memory, system prompts, retrieval, and evaluations.
 * Own features across a consumer chat app and its backend, from design and implementation through launch and iteration.
 * Work with the founding team to bring chat, agents, tools, and app capabilities into a cohesive consumer product.
 * Expand agent capabilities with virtual machines and advanced tooling, and develop backend and database infrastructure for scale.
 * Help deploy and optimize GPU infrastructure, including fine-tuning and serving efficient open-source models.
 * Balance latency, cost, reliability, privacy, and user experience across product and infrastructure decisions.