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# Senior Applied Scientist, Annapurna ML

- **Company:** Amazon.com, Inc.
- **Location:** Cupertino, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Mxnet, Java (Programming Language), Business Software, C++ (Programming Language), Distributed Systems, R (Programming Language), Apache Hadoop, Python (Programming Language), Machine Learning, NumPy, Software Tools, Tensorflow, SciPy, Pytorch, Apache Spark, Deep Learning, AWS Trainium, Spark Mllib, Scikit Learn
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_e2ec7697bd10a362bd7acb79bde6e7de

## About the Role

* 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, * Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
 * Experience with large scale distributed systems such as Hadoop, Spark etc.

## Description

* 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., * 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.