Design, develop, and deploy autonomous AI agents and LLM-powered systems for resume optimization, job matching, career coaching, and AI interviews. Build multi-agent workflows, RAG systems, vector databases, prompt engineering and fine-tuning pipelines, agent evaluations, and human-in-the-loop oversight. Integrate LLMs into web applications and browser extensions while ensuring production scalability and reliability. Collaborate with engineering, product, design, and company leadership, and develop traditional machine learning workflows where needed.

Design and build reinforcement-learning training environments and diverse tasks to evaluate and improve LLM agents; iterate rapidly on task designs from customer feedback, deliver high-quality outputs with minimal supervision, and maintain PST overlap for collaboration.

Create realistic computer-use tasks for AI agents on Linux desktops. Responsibilities include designing multi-step scenarios, preparing files and documents, writing agent instructions, developing Python setup and scoring scripts, testing environments, and documenting tasks. Requires Python, daily AI tool usage, basic AI/ML experience, Ubuntu and virtual machine familiarity, attention to detail, and Git basics. Experience building RL environments or computer-use benchmarks is preferred.