Principal Research Scientist - AI Scaling & Optimization
Leads research team advancing LLM scaling, efficiency, post-training, RL, and inference optimization. Drives innovations from research to production, requiring deep ML expertise, Python/PyTorch proficiency, and leadership in large-scale experiments.
270k – 350k
On-siteAI Research
About the role
Responsibilities
Lead and grow a multidisciplinary research team focused on LLM scaling, efficiency, and systems performance.
Define scaling research roadmap aligned with strategic objectives, prioritizing foundation model efficiency and large-scale training/inference.
Drive algorithmic innovations for neural network training/inference (optimizers, low-precision techniques, model adaptation) with empirical validation.
Optimize end-to-end ML systems for distributed training, RL, memory/compute efficiency in collaboration with systems/platform teams.
Partner with product/engineering to productionize research breakthroughs in scaling/efficiency.
Represent research externally via publications, talks, collaborations.
Mentor talent with technical/career guidance.
What You Will Do
Define/lead research programs on foundation model efficiency (optimizer design, low-precision training/inference, scalable architectures, adaptation methods).
Leads research team advancing LLM scaling, post-training, RL, and inference efficiency. Drives innovations in optimization, distributed systems, and production integration using Python/PyTorch, with deep expertise in large-scale ML.
280k – 350k
HybridAI Research
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