Description
We're looking for an experienced Machine Learning consultant with deep expertise in Large Language Models (LLMs), prompt engineering, evaluation frameworks, and text generation. Our team has built a system focused on making LLM-generated writing feel genuinely human. Before making changes, we need someone who can first understand exactly what problem we're solving, review our existing approach, identify strengths and weaknesses, and recommend the most effective path forward. This is a research-first consulting engagement. Depending on your findings, it may evolve into hands-on implementation work. What you'll do: Deep dive into our current system and understand the exact problem we're solving. Review our existing architecture, prompting strategy, evaluation methods, and ML pipeline. Recommend improvements backed by research and practical experience. If needed, help implement or prototype solutions. Advise us on best practices for making LLM-generated writing more natural, human, and reliable. You should have experience with: Large Language Models (OpenAI, Anthropic, Gemini, open-source models, etc.) Prompt engineering and prompt optimization LLM evaluation and benchmarking Human-like text generation RAG, fine-tuning, preference optimization, or related techniques (where applicable) Building or improving production ML/LLM pipelines Your proposal must include: Projects you've worked on that are similar to this problem. Proof of work whenever possible (GitHub, case studies, pape