Featherless AI
1 - 10 employees
Founded 2023
🤖 Artificial Intelligence
☁️ SaaS
🔌 API
Artificial Intelligence • SaaS • API
Featherless AI is a serverless AI inference and model hosting provider that offers API access to a large and growing catalog of open-weight models (12,200+), enabling developers and businesses to deploy, fine-tune, and run models at scale without managing servers. The company provides flat subscription pricing with unlimited tokens, GPU orchestration, private/anonymous usage (no logs), and options for enterprise self-hosting or scale units for high concurrency. Featherless AI also operates as an AI research lab focused on open-source and post-transformer model research, claiming significant cost and performance improvements for large models and AI agents.
AI Researcher – Distillation
Job not on LinkedIn
🕒 January 23
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Featherless AI
1 - 10 employees
Founded 2023
🤖 Artificial Intelligence
☁️ SaaS
🔌 API
Artificial Intelligence • SaaS • API
Featherless AI is a serverless AI inference and model hosting provider that offers API access to a large and growing catalog of open-weight models (12,200+), enabling developers and businesses to deploy, fine-tune, and run models at scale without managing servers. The company provides flat subscription pricing with unlimited tokens, GPU orchestration, private/anonymous usage (no logs), and options for enterprise self-hosting or scale units for high concurrency. Featherless AI also operates as an AI research lab focused on open-source and post-transformer model research, claiming significant cost and performance improvements for large models and AI agents.
📋 Description
• Design and evaluate **model distillation techniques** (teacher–student training, self-distillation, layer-wise distillation, representation matching, etc.) • Research tradeoffs between **model size, latency, memory, and accuracy** • Develop novel distillation approaches for: • - Large language models • - Long-context or specialized architectures • - Inference-constrained environments • Run large-scale experiments and ablations; analyze results rigorously • Collaborate with engineers to **productionize research outcomes** • Write and submit **research papers** to top-tier venues (NeurIPS, ICML, ICLR, COLM, etc.) • Contribute to internal research notes, technical blogs, and open-source projects when appropriate
🎯 Requirements
• Strong background in **machine learning research** • Hands-on experience with **model distillation** or closely related topics (compression, pruning, quantization, representation learning) • **Publication experience** (conference or journal papers, workshop papers, or arXiv preprints) • Solid understanding of deep learning fundamentals (optimization, training dynamics, generalization) • Fluency in **PyTorch** (or equivalent) and research-grade experimentation • Ability to clearly communicate research ideas, results, and limitations • Experience distilling **large language models** (nice to have) • Work on efficiency-focused research (latency, memory, throughput) (nice to have) • Experience with long-context models or non-Transformer architectures (nice to have) • Open-source contributions in ML or research tooling (nice to have) • Prior startup or applied research experience (nice to have)
🏖️ Benefits
• Real ownership over research direction at a **Series A stage** • Strong support for **publishing and open research** • Tight feedback loop between research and real-world deployment • Access to meaningful compute and production-scale problems • Small, highly technical team with deep ML and systems expertise
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