Applied Scientist II, AWS Neuron Science - Core Algorithm

• 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 system that gives 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.
• A day in the life
• About the team
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• 3+ years of building models for business application experience
• Experience programming in Java, C++, Python or related language
• Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
• Experience in professional software development
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
• Knowledge of standard speech and machine learning techniques
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
• USA, CA, Cupertino - 171,600.00 - 222,200.00 USD annually

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