Applied Scientist, Hardware Devices Science Team
Job Description
Amazon Devices is an inventive research and development company that designs and engineer high-profile devices like the Kindle family of products, Fire Tablets, Fire TV, Health Wellness, Amazon Echo & Astro products. This is an exciting opportunity to join Amazon in developing state-of-the-art techniques that bring Gen AI on edge for our consumer products. We are looking for an exceptional Applied Scientist to join our team and help develop the next generation of edge models, and optimize them while doing co-designed with custom ML HW based on a revolutionary architecture.Key job responsibilities- Engage in state-of-the-art and innovative research in areas such as Gen AI, model compression, and knowledge distillation- Contribute to a novel and comprehensive training platform custom-tailored for preparing models for edge applications- Invent optimization techniques to push the boundaries of deep learning model training- Derive research approaches from first principles via knowledge of Information Theory, Statistics, Scientific Computing, and Deep Learning Theory- Create and propose detailed theoretical specifications for novel research ideas and directions, and rigorously justify their correctness- Train custom Gen AI models that beat the SOTA and paves 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 our devices by cohesively unifying software and hardware- Publish in open source and present on Amazon's behalf at key ML conferences - e.g. NeurIPS, ICLR, MLSys
Locations
- United Kingdom, Cambridge, Cambridge, England, United Kingdom
Salary
Salary not disclosed
Estimated Salary Rangemedium confidence
85,000 - 140,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- - Experience applying theoretical models in an applied environmentintermediate
- - Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruningintermediate
- - Experience implementing algorithms using toolkits and self-developed codeintermediate
- - Experience with programming languages such as Python, Java, C++intermediate
Required Qualifications
- - PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field (degree in engineering)
- - Experience applying theoretical models in an applied environment (experience)
- - Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning (experience)
- - Experience implementing algorithms using toolkits and self-developed code (experience)
- - Experience with programming languages such as Python, Java, C++ (experience)
Preferred Qualifications
- - Experience in professional software development (experience)
- - Experience building machine learning models or developing algorithms for business application (experience)
Responsibilities
- - Engage in state-of-the-art and innovative research in areas such as Gen AI, model compression, and knowledge distillation
- - Contribute to a novel and comprehensive training platform custom-tailored for preparing models for edge applications
- - Invent optimization techniques to push the boundaries of deep learning model training
- - Derive research approaches from first principles via knowledge of Information Theory, Statistics, Scientific Computing, and Deep Learning Theory
- - Create and propose detailed theoretical specifications for novel research ideas and directions, and rigorously justify their correctness
- - Train custom Gen AI models that beat the SOTA and paves 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 our devices by cohesively unifying software and hardware
- - Publish in open source and present on Amazon's behalf at key ML conferences - e.g. NeurIPS, ICLR, MLSys
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Applied Scientist, Hardware Devices Science Team
Job Description
Amazon Devices is an inventive research and development company that designs and engineer high-profile devices like the Kindle family of products, Fire Tablets, Fire TV, Health Wellness, Amazon Echo & Astro products. This is an exciting opportunity to join Amazon in developing state-of-the-art techniques that bring Gen AI on edge for our consumer products. We are looking for an exceptional Applied Scientist to join our team and help develop the next generation of edge models, and optimize them while doing co-designed with custom ML HW based on a revolutionary architecture.Key job responsibilities- Engage in state-of-the-art and innovative research in areas such as Gen AI, model compression, and knowledge distillation- Contribute to a novel and comprehensive training platform custom-tailored for preparing models for edge applications- Invent optimization techniques to push the boundaries of deep learning model training- Derive research approaches from first principles via knowledge of Information Theory, Statistics, Scientific Computing, and Deep Learning Theory- Create and propose detailed theoretical specifications for novel research ideas and directions, and rigorously justify their correctness- Train custom Gen AI models that beat the SOTA and paves 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 our devices by cohesively unifying software and hardware- Publish in open source and present on Amazon's behalf at key ML conferences - e.g. NeurIPS, ICLR, MLSys
Locations
- United Kingdom, Cambridge, Cambridge, England, United Kingdom
Salary
Salary not disclosed
Estimated Salary Rangemedium confidence
85,000 - 140,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- - Experience applying theoretical models in an applied environmentintermediate
- - Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruningintermediate
- - Experience implementing algorithms using toolkits and self-developed codeintermediate
- - Experience with programming languages such as Python, Java, C++intermediate
Required Qualifications
- - PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field (degree in engineering)
- - Experience applying theoretical models in an applied environment (experience)
- - Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning (experience)
- - Experience implementing algorithms using toolkits and self-developed code (experience)
- - Experience with programming languages such as Python, Java, C++ (experience)
Preferred Qualifications
- - Experience in professional software development (experience)
- - Experience building machine learning models or developing algorithms for business application (experience)
Responsibilities
- - Engage in state-of-the-art and innovative research in areas such as Gen AI, model compression, and knowledge distillation
- - Contribute to a novel and comprehensive training platform custom-tailored for preparing models for edge applications
- - Invent optimization techniques to push the boundaries of deep learning model training
- - Derive research approaches from first principles via knowledge of Information Theory, Statistics, Scientific Computing, and Deep Learning Theory
- - Create and propose detailed theoretical specifications for novel research ideas and directions, and rigorously justify their correctness
- - Train custom Gen AI models that beat the SOTA and paves 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 our devices by cohesively unifying software and hardware
- - Publish in open source and present on Amazon's behalf at key ML conferences - e.g. NeurIPS, ICLR, MLSys