Principal Applied Scientist, Hardware Silicon and Systems Group
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Job Location:
Monthly Salary:
Posted on:
30+ days ago
Vacancies:
1 Vacancy
Job Summary
Hardware Silicon and Systems Group leads the development and optimization of on-device ML models for Amazons hardware products including audio vision and multi-modal AI features. We work at the critical intersection of ML innovation and silicon design ensuring AI capabilities can run efficiently on resource-constrained devices.
Currently we enable production ML models across multiple device families including Echo Ring/Blink and other consumer devices. Our work directly impacts Amazons customer experiences in consumer AI device market. The solutions we develop determine which AI features can be offered on-device versus requiring cloud connectivity ultimately shaping product capabilities and customer experience across Amazons hardware portfolio.
This is a unique opportunity to help shape the future of AI in consumer devices at unprecedented scale. Youll be at the forefront of developing industry-first model architectures and compression techniques that will power AI features across millions of Amazon devices worldwide. Your innovations will directly enable new AI features that enhance how customers interact with Amazon products every day.
As Principal Applied Scientist you will blend expertise at the intersection of ML and hardware optimization for model training build cutting-edge architectures for vision language and multi-modal tasks. Role requires a specialist in hardware-aware quantization with hands-on experience in model compression techniques like pruning and distillation. You will be responsible for computer architecture ML accelerator designs efficient inference algorithms and low-precision arithmetic.
Key job responsibilities
As a Principal Applied Scientist you will:
Own the technical architecture and optimization strategy for ML models deployed across Amazons device ecosystem from existing to yet-to-be-shipped products.
Develop novel model architectures optimized for our custom silicon establishing new methodologies for model compression and quantization.
Create an evaluation framework for model efficiency and implement multimodal optimization techniques that work across vision language and audio tasks.
Define technical standards for model deployment and drive research initiatives in model efficiency to guide future silicon designs.
Spend the majority of your time doing deep technical work - developing novel ML architectures writing critical optimization code and creating proof-of-concept implementations that demonstrate breakthrough efficiency gains.
Influence architecture decisions impacting future silicon generations establish standards for model optimization and mentor others in advanced ML techniques.
Basic Qualifications:
Masters degree in Computer Science Electrical Engineering or a related technical field
8 years of experience in machine learning with a focus on model architecture design optimization and deployment
Expertise in developing and deploying deep learning models for real-world applications including vision language and multi modal tasks
Strong background in computer architecture hardware acceleration and efficient inference algorithms
Hands-on experience with model compression techniques such as pruning quantization and distillation
Proficiency with deep learning frameworks like TensorFlow PyTorch or ONNX
PhD in Computer Science Electrical Engineering or a related technical field
10 years of experience in machine learning with a track record of developing novel model architectures and optimization techniques
Proven expertise in co-designing ML models and hardware accelerators for efficient on-device inference
In-depth understanding of the latest advancements in model compression including techniques like knowledge distillation network pruning and hardware-aware quantization
Experience working on resource-constrained embedded systems and deploying ML models on edge devices
Demonstrated ability to influence technical strategy and mentor cross-functional teams
Strong communication skills and the ability to effectively present complex technical concepts to both technical and non-technical stakeholders
Our inclusive culture empowers Amazonians to deliver the best results for our 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 for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.
Currently we enable production ML models across multiple device families including Echo Ring/Blink and other consumer devices. Our work directly impacts Amazons customer experiences in consumer AI device market. The solutions we develop determine which AI features can be offered on-device versus requiring cloud connectivity ultimately shaping product capabilities and customer experience across Amazons hardware portfolio.
This is a unique opportunity to help shape the future of AI in consumer devices at unprecedented scale. Youll be at the forefront of developing industry-first model architectures and compression techniques that will power AI features across millions of Amazon devices worldwide. Your innovations will directly enable new AI features that enhance how customers interact with Amazon products every day.
As Principal Applied Scientist you will blend expertise at the intersection of ML and hardware optimization for model training build cutting-edge architectures for vision language and multi-modal tasks. Role requires a specialist in hardware-aware quantization with hands-on experience in model compression techniques like pruning and distillation. You will be responsible for computer architecture ML accelerator designs efficient inference algorithms and low-precision arithmetic.
Key job responsibilities
As a Principal Applied Scientist you will:
Own the technical architecture and optimization strategy for ML models deployed across Amazons device ecosystem from existing to yet-to-be-shipped products.
Develop novel model architectures optimized for our custom silicon establishing new methodologies for model compression and quantization.
Create an evaluation framework for model efficiency and implement multimodal optimization techniques that work across vision language and audio tasks.
Define technical standards for model deployment and drive research initiatives in model efficiency to guide future silicon designs.
Spend the majority of your time doing deep technical work - developing novel ML architectures writing critical optimization code and creating proof-of-concept implementations that demonstrate breakthrough efficiency gains.
Influence architecture decisions impacting future silicon generations establish standards for model optimization and mentor others in advanced ML techniques.
Basic Qualifications:
Masters degree in Computer Science Electrical Engineering or a related technical field
8 years of experience in machine learning with a focus on model architecture design optimization and deployment
Expertise in developing and deploying deep learning models for real-world applications including vision language and multi modal tasks
Strong background in computer architecture hardware acceleration and efficient inference algorithms
Hands-on experience with model compression techniques such as pruning quantization and distillation
Proficiency with deep learning frameworks like TensorFlow PyTorch or ONNX
PhD in Computer Science Electrical Engineering or a related technical field
10 years of experience in machine learning with a track record of developing novel model architectures and optimization techniques
Proven expertise in co-designing ML models and hardware accelerators for efficient on-device inference
In-depth understanding of the latest advancements in model compression including techniques like knowledge distillation network pruning and hardware-aware quantization
Experience working on resource-constrained embedded systems and deploying ML models on edge devices
Demonstrated ability to influence technical strategy and mentor cross-functional teams
Strong communication skills and the ability to effectively present complex technical concepts to both technical and non-technical stakeholders
Our inclusive culture empowers Amazonians to deliver the best results for our 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 for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.
Required Experience:
Staff IC
Key Skills
- Machine Learning
- Python
- Data Science
- AI
- R
- Research Experience
- Sensors
- Drug Discovery
- Research & Development
- Natural Language Processing
- Data Analysis Skills
- Toxicology Experience
About Company
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