Featherless AI — AI Researcher — Training Optimization
About the role
Featherless AI is hiring an AI Researcher to improve how open-source LLMs are trained and fine-tuned. You will research training efficiency methods, analyze training dynamics across model families, and apply findings that improve the quality of models hosted on the Featherless platform.
What you'll do
- Research training efficiency techniques including learning rate schedules, data mixing, and curriculum learning
- Design controlled experiments to evaluate fine-tuning strategies at scale across diverse model families
- Analyze training dynamics and failure modes to improve stability and convergence
- Collaborate with engineering to integrate training research insights into the platform
- Contribute to open-source training codebases and potentially publish research findings
Requirements
- Research background in LLM training, fine-tuning, or efficient adaptation methods
- Familiarity with parameter-efficient fine-tuning (LoRA, QLoRA, DoRA, etc.)
- Proficiency in Python with PyTorch or JAX; experience with distributed training
- Strong experimental design skills and fluency with training loss analysis
About Featherless AI
Featherless AI is a serverless inference platform hosting 3,000+ open-source LLMs, letting developers call any model via a simple API without managing GPU infrastructure.
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