AI Researcher

Featherlessai; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Featherlessai is seeking a dedicated machine learning researcher to advance distillation techniques for large language models and long-context architectures. You will design teacher–student and self-distillation methods, compare tradeoffs among size, latency, and accuracy, and iterate on novel approaches with an emphasis on rigorous analysis.

You will run large-scale experiments, collaborate with engineers to productionize results, and contribute to research notes and blogs.

Qualifications

• Strong background in ML research required.
• Hands-on experience with model distillation, compression, pruning, or quantization.
• Publication experience in conferences, journals, or arXiv preprints.
• Solid understanding of deep learning fundamentals including optimization and training dynamics.
• Fluency in PyTorch or equivalent frameworks.
• Ability to communicate research ideas and results clearly.

Responsibilities

• Design and evaluate model distillation techniques including teacher–student training, self-distillation, and representation matching.
• Research tradeoffs between model size, latency, memory, and accuracy.
• Develop novel distillation approaches for large language models and long-context architectures.
• Run large-scale experiments and ablations with rigorous analysis.
• Collaborate with engineers to productionize research outcomes.
• Write and submit research papers to top-tier venues such as NeurIPS, ICML, ICLR, and COLM.
• Contribute to internal research notes, technical blogs, and open-source projects.

Skills

Machine learning research

Publication experience

Communication of results

Distillation methods

Tools

PyTorch

Job description

Responsibilities

• Design and evaluate model distillation techniques including teacher–student training, self-distillation, and representation matching
• Research tradeoffs between model size, latency, memory, and accuracy
• Develop novel distillation approaches for large language models and long-context architectures
• Run large-scale experiments and ablations with rigorous analysis
• Collaborate with engineers to productionize research outcomes
• Write and submit research papers to top-tier venues such as NeurIPS, ICML, ICLR, and COLM
• Contribute to internal research notes, technical blogs, and open-source projects

Requirements

• Strong background in machine learning research
• Hands-on experience with model distillation, compression, pruning, or quantization
• Publication experience in conferences, journals, or arXiv preprints
• Solid understanding of deep learning fundamentals including optimization and training dynamics
• Fluency in PyTorch or equivalent frameworks
• Ability to communicate research ideas and results clearly

Preferred Qualifications

• Experience distilling large language models
• Experience with efficiency-focused research regarding latency and throughput
• Experience with long-context models or non-Transformer architectures
• Open-source contributions in ML or research tooling
• Prior startup or applied research experience