AI Researcher – Training Optimization

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🕒 January 23

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Featherless AI

WebsiteLinkedInAll Job Openings

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 training optimization techniques for large models (e.g. optimization algorithms, schedulers, normalization, curriculum strategies) • Improve training efficiency and stability across long runs and large datasets • Research and implement methods such as: • Optimizer and scheduler innovations • Mixed-precision, low-precision, and memory-efficient training • Gradient noise reduction, scaling laws, and convergence analysis • Training-time regularization and robustness techniques • Run large-scale experiments, analyze results, and translate findings into actionable improvements • Author or co-author research papers, technical reports, or blog posts • Collaborate closely with infrastructure and inference teams to ensure training decisions translate to real-world performance

🎯 Requirements

• Strong background in machine learning research, with emphasis on training dynamics and optimization • Experience training large neural networks (LLMs, multimodal models, or large sequence models) • Publication experience in ML venues (e.g. NeurIPS, ICML, ICLR, ACL, EMNLP, COLM, arXiv) or equivalent high-quality open research • Solid understanding of: • Optimization theory and practice • Backpropagation, gradient flow, and training stability • Distributed and large-batch training • Proficiency in Python and modern ML frameworks (PyTorch preferred) • Ability to independently design experiments and reason from data • Nice to Have • Experience with non-standard architectures (e.g. RNN variants, long-context models, hybrid systems) • Experience optimizing training on GPUs at scale (FSDP, ZeRO, custom kernels) • Contributions to open-source ML or research codebases • Comfort operating in fast-moving, ambiguous startup environments

🏖️ Benefits

• Freedom to pursue and publish novel research • Real influence over core model training decisions • Direct access to large-scale experiments and real production constraints • A small, senior team that values thinking deeply and shipping thoughtfully