AI Engineer

Responsibilities

• 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

Requirements

• 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

Tech Stack

• Next.js, GraphQL, Node, OpenAI, Anthropic