LLM Python Engineer
Data Curation & Synthetic Generation: Build high-quality datasets for Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). This includes writing clean Python code for AI training and creating "ground truth" solutions. Model Evaluation: Analyze and rank AI model responses based on technical accuracy, safety, and reasoning. You may also perform "human-in-the-loop" testing to identify practical issues automated tests miss. Repository Validation: Manage real-world codebases to evaluate how LLMs handle complex bug-fixing and feature development. This involves triaging GitHub issues and ensuring code adheres to unit test quality. Tool Development: Design lightweight Python-based utilities and automation scripts to help teams efficiently review and validate large-scale datasets.