Senior Applied Scientist, AWS Neuron Science - Core Algorithm
# Senior Applied Scientist, AWS Neuron Science - Core Algorithm **Annapurna Labs (U.S.) Inc.** · Cupertino, California, USA · `On-site` · `full-time` 🕒 **Статус:** *Опубликовано: вчера* · *Источник: Amazon* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Applied Science]` `[Applied Science]` --- ### About the Role Description The AWS Neuron Science Core Algorithm team is looking for talented Applied Scientists to push the frontier of hardware-aware machine learning for Trainium and Inferentia, the AWS Machine Learning accelerators. In this rare role at the intersection of LLM modeling, large-scale training systems, and hardware/datatype co-design, you own model and algorithm decisions jointly with AWS custom silicon. You will own solutions end-to-end from research through production, publish at top venues, and work alongside distinguished engineers and scientists in a strategic growth area for AWS. We actively work on these areas: - Low-precision training and inference: MXFP8, MXFP4, and sub-4-bit training and inference recipes, stochastic rounding, and Trn4 datatype exploration. - Trn-friendly architectures: model architectures that exploit hardware strengths without sacrificing quality. - System-aware optimizers & efficient distributed systems: efficient optimizers and distributed systems that give the best accuracy, co-designed with the hardware. - Foundation-model pre-training accuracy: end-to-end validation across model scales, catching training divergence early, and equivalence-checking tooling. - GenAI for systems: RL post-training for NKI kernel generation, mitigating reward-hacking and accelerating under low precision on Trn. Key job responsibilities - Own scientific problems end-to-end - from research and experimentation through production impact - applying rigorous evaluation to complex, ill-defined problems at large scale. - Develop production-quality code in PyTorch or JAX and integrate scientific components into large-scale training and inference systems with operational excellence and efficient resource usage. - Partner with foundation-model, engineering, and hardware-architecture teams so your findings directly inform what gets built into Trainium and shipped in the product stack. - Mentor fellow scientists and interns, give constructive peer reviews, and help shape team goals, priorities, and the technical roadmap. - Author and publish research at top peer-reviewed venues (ICLR, NeurIPS, ICML, MLSys) and engage the broader scientific community. - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - 3+ years of building machine learning models for business application experience - Experience with neural deep learning methods and machine learning
- Applied Science
Наблюдалась 2026-10-07, впервые 2026-10-06, найдена на 2 площадках, источник — Amazon.