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# AI Engineer

- **Company:** Vooma Inc.
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Short Message Service, TypeScript, Enterprise Software Applications, Large Language Models, Data Generation
- **Published:** November 6, 2026
- **Apply:** https://www.careerbuilder.com/job-details/ai-engineer-san-francisco-ca--eb3ce1b8-3f3e-4cc4-9b77-98d706545123

## About the Role

* Hands-on experience working with modern foundation models (LLMs, multimodal models, or similar) in production settings
 * Strong intuition for model behavior, evaluation, and failure modes
 * Experience with fine-tuning, training pipelines, and dataset construction
 * Familiarity with techniques like RLHF, synthetic data generation, or active learning (not required, but highly relevant)
 * Comfort working across Python-based ML stacks and Typescript-based production systems
 * A bias toward action - you run experiments, measure results, and iterate quickly
 * The mindset of an owner: you care about outcomes, not just outputs, and push systems to actually work in the real world

## Description

Your role, should you choose to join us, will be an AI Engineer on our founding team.

Youre the right person for this role if youre excited to work at the frontier of applied AI, training and iterating advanced models that work in the real world. You're driven by turning cutting-edge capabilities into reliable, high-performance systems that deliver highly scaled impact.

Youre a great fit if youre known for going deep on model behavior and building systems that continuously improve through well-designed data flywheels. You find energy in shipping fast, learning from real usage, and iterating toward systems that feel almost magical in their effectiveness.

Your responsibilities will include:

 * Training, fine-tuning, and deploying state-of-the-art multimodal models across a range of real-world tasks
 * Designing and implementing evaluation frameworks to rigorously measure model performance and guide iteration
 * Building and scaling data flywheels - collecting, curating, and generating high-quality datasets to continuously improve model outcomes
 * Developing systems for live learning, feedback incorporation, and continuous model adaptation in production
 * Implementing techniques like negative mining to harden models against edge cases and failure modes
 * Owning the full lifecycle from experimentation validation deployment monitoring
 * Collaborating closely across engineering and product to integrate models into reliable, high-performance systems