Software Engineer

Venture Up; San Francisco (CA); Hybrid; USD 350,000 - 600,000; Full time

Benefits offered by this job

Competitive equity

SF FiDi office

Remote Sunday

Job summary

Venture Up is seeking a Software Engineer to build infrastructure for frontier AI labs in San Francisco. You’ll own end-to-end projects, tackle distributed systems, and work across stack technologies such as Python, TypeScript, React, Node.js, Kubernetes, and Kafka.

Join a fast-paced, early-stage environment with strong equity incentives and in-office SF work and a Sunday remote option. You’ll contribute to RL environments, evaluation systems and data infrastructure powering AI research, while

Qualifications

• 1–4 years of strong software engineering experience.
• Strong full-stack fundamentals across Python, TypeScript, React and Node.js.
• Comfortable tackling infrastructure and distributed systems problems using Kubernetes and Kafka.
• High ownership, intellectually curious and able to move quickly across unfamiliar technical problems.
• Excited by an intense, early-stage environment and working in-person alongside other ambitious engineers.

Responsibilities

• Build for Frontier AI – Develop RL environments, evaluation systems and infrastructure powering data creation for advanced AI and agentic reasoning.
• Own Projects End-to-End – Move from prototyping ideas from research papers through to building and deploying production systems.
• Solve Hard Engineering Problems – Work across Kubernetes, Kafka, APIs, distributed systems and full-stack applications operating at significant scale.
• Work Across the Stack – Collaborate directly with research, product and design, tackling whatever engineering problem has the highest impact.

Skills

Python

TypeScript

React

Node.js

Kubernetes

Kafka

Distributed systems

Job description

Software Engineer: Build Infrastructure for Frontier AI Labs | $350K–$600K OTE

Do you want to work on the infrastructure pushing forward the capabilities of LLMs and AI agents? Do you want genuine end-to-end ownership, moving between AI research, distributed systems and product engineering rather than being confined to one part of the stack?

A $34M-funded AI startup in San Francisco is building the data and evaluation infrastructure used by leading AI labs to develop increasingly capable models and agents. Growing rapidly, they've assembled a highly technical team from companies including Google, Meta and Jane Street.

They're looking for Software Engineers with 1–4 years of experience who want to solve difficult engineering problems and take significant ownership early in their careers.

What You'll Do

• Build for Frontier AI – Develop RL environments, evaluation systems and infrastructure powering data creation for advanced AI and agentic reasoning.
• Own Projects End-to-End – Move from prototyping ideas from research papers through to building and deploying production systems.
• Solve Hard Engineering Problems – Work across Kubernetes, Kafka, APIs, distributed systems and full-stack applications operating at significant scale.
• Work Across the Stack – Collaborate directly with research, product and design, tackling whatever engineering problem has the highest impact.

What They're Looking For

• 1–4 years of strong software engineering experience.
• Strong full-stack fundamentals across technologies such as Python, TypeScript, React and Node.js.
• Comfortable tackling infrastructure and distributed systems problems using technologies such as Kubernetes and Kafka.
• High ownership, intellectually curious and able to move quickly across unfamiliar technical problems.
• Excited by an intense, early-stage environment and working in-person alongside other ambitious engineers.

Why Join?

• $350,000–$600,000 OTE, with uncapped upside and competitive equity.
• Five days per week in their San Francisco FiDi office, plus a half-day or remote Sunday.
• $34M raised, rapid growth and already working with leading AI labs on infrastructure at the frontier of model development.

This isn't a role where you'll spend years working on one narrow piece of a mature system. You'll move between AI research, infrastructure and product engineering while building systems directly contributing to the next generation of AI models.