Job Purpose
To develop tools and provide insights that enhance the training and refinement of large language models (LLMs) through Reinforcement Learning from Human Feedback (RLHF), leveraging Rust development skills and an interest in machine learning and human-computer interaction.
Roles And Responsibilities
- Contribute to RLHF tasks by delivering human feedback on LLM outputs, aiding in model training.
- Evaluate and provide qualitative feedback on LLM responses to identify improvement areas.
- Collaborate with data scientists and ML engineers to integrate feedback into the model's learning process.
- Work with cross-functional teams to understand and implement human feedback requirements.
- Experiment with new methods for collecting and integrating human feedback in RLHF tasks.
- Document best practices for providing and incorporating feedback into LLM training.
- Stay updated on advancements in reinforcement learning, LLMs, and human-computer interaction.
Must-Have Qualifications
- 3+ years of experience in Rust development, with strong skills in systems programming, performance optimization, and concurrency.
- Familiarity with machine learning concepts, especially reinforcement learning.
- Experience in human-computer interaction or user feedback systems is a plus.
- Proficiency with version control systems like Git and CI/CD pipelines.
- Strong analytical skills for improving RLHF task efficiency and providing constructive AI/ML feedback.
- Excellent communication skills, detail-oriented approach, and a proactive mindset.
- Bachelor's degree in Computer Science, Engineering, or related field, or equivalent work experience.
