Kotlin / Android Developer

• This position is within a project with one of the foundational LLM companies. The goal is to assist these foundational LLM companies in enhancing their Large Language Models. One way we help these companies improve their models is by providing them with high-quality proprietary data.
• This data serves two main purposes: first, as a basis for fine-tuning their models, and second, as an evaluation set to benchmark the performance of their models or competitor models.
• For example, for SFT data generation, you might have to put together or be provided a prompt which contains provided code and questions, you will then provide the model responses, and write corresponding Swift code to solve the questions.
• For RLHF data generation, you may need to create a prompt yourself or use one provided by the customer, ask the model questions, and evaluate the outputs generated by two versions of the LLM. Youll compare these outputs and provide feedback, which is then used to fine-tune the models. Please note that this role does not involve building or fine-tuning LLMs.

What does day-to-day look like:

• Design, develop, and maintain efficient, high-quality code to train and optimize AI models.
• Conduct evaluations (Evals) to benchmark model performance and analyze results for continuous improvement.
• Evaluate and rank AI model responses to user queries across diverse domains, ensuring alignment with predefined criteria.
• Develop comprehensive explanations and rationales for evaluations, showcasing excellent reasoning and technical expertise.
• Lead efforts in Supervised Fine-Tuning (SFT), including creating and maintaining high-quality, task-specific datasets.
• Collaborate with researchers and annotators to execute Reinforcement Learning with Human Feedback (RLHF) and refine reward models.
• Design innovative evaluation strategies and processes to improve the models alignment with user needs and ethical guidelines.
• Create and refine optimal responses to improve AI performance, emphasizing clarity, relevance, and technical accuracy.
• Conduct thorough peer reviews of code and documentation, providing constructive feedback and identifying areas for improvement.
• Collaborate with cross-functional teams to improve model performance and contribute to product enhancements.
• Continuously explore and integrate new tools, techniques, and methodologies to enhance AI training processes.