Optimize AI for Edge Computing
We are looking for exceptional scientists to join our team and help develop the next generation of edge models, optimizing them while co-designed with custom ML HW based on a revolutionary architecture. The role involves quantizing, pruning, distilling, and finetuning Gen AI models to optimize for edge platforms.
Job Responsibilities:
* Quantize, prune, distill, finetune Gen AI models to optimize for edge platforms
* Fundamentally understand Amazon's underlying Neural Edge Engine to invent optimization techniques
* Analyze deep learning workloads and provide guidance to map them to Amazon's Neural Edge Engine
* Use first principles of Information Theory, Scientific Computing, Deep Learning Theory, Non Equilibrium Thermodynamics
* Train custom Gen AI models that beat SOTA and pave the path for developing production models
* Collaborate closely with compiler engineers, fellow Applied Scientists, Hardware Architects, and product teams to build the best ML-centric solutions for devices
* Publish in open source and present on behalf at key ML conferences - NeurIPS, ICLR, MLSys
Qualifications:
* 3+ years of building machine learning models for business application experience
* PhD or Master's degree and 6+ years of applied research experience
* Experience programming in Java, C++, Python, or related language
* Experience with neural deep learning methods and machine learning
* Preferred: 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.
Our Culture:
Our inclusive culture empowers employees to deliver the best results for customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit the link below for more information.