Principal Applied Scientist, Hardware Silicon and Systems Group

Our team 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 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. Come join our team!



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.

This role requires a blend of expertise at the intersection of ML and hardware optimization. You must be an expert in model training with deep knowledge of cutting-edge architectures for vision language and multimodal tasks. Crucially you need to be a specialist in hardware-aware quantization with hands-on experience in model compression techniques like pruning and distillation. A strong background in computer architecture and familiarity with ML accelerator designs is essential as is expertise in efficient inference algorithms and low-precision arithmetic.
Basic Qualifications:
Advanced degree (PhD preferred) 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 multimodal 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

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees supervisors and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees supervisors and staff to ensure exceptional customer service; and follow all federal state and local laws and Company policies. Criminal history may have a direct adverse and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above as well as the abilities to adhere to company policies exercise sound judgment effectively manage stress and work safely and respectfully with others exhibit trustworthiness and professionalism and safeguard business operations and the Companys reputation. Pursuant to the Los Angeles County Fair Chance Ordinance we will consider for employment qualified applicants with arrest and conviction records.

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Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $179000/year in our lowest geographic market up to $309400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge skills and experience. Amazon is a total compensation company. Dependent on the position offered equity sign-on payments and other forms of compensation may be provided as part of a total compensation package in addition to a full range of medical financial and/or other benefits. For more information please visit This position will remain posted until filled. Applicants should apply via our internal or external career site.