Amazon Principal Applied Scientist OPD Hardware 

United States, California, Sunnyvale 

745283816

About the Role:
Key job responsibilities
Key Responsibilities:* Drive strategic vision and long-term roadmap for camera and sensor technologies, influencing product teams and aligning direction across technology, silicon, and devices organizations
* Lead groundbreaking ML/CV innovations, focusing on differentiated hardware technologies for advanced camera and sensor capabilities
* Architect and implement state-of-the-art ML models for edge computing in consumer products, optimizing for performance and efficiency
* Guide the development of next-generation AI-enabled devices, including wearables, Echo devices, Fire TVs, and robotics
* Guide the invention and design of next-generation wearables, Echo devices, Fire TVs, and robotics
* Collaborate with cross-functional teams to integrate ML solutions into hardware designs
* Mentor and develop team members, fostering innovation and technical excellence
We're committed to fostering an inclusive environment where everyone can do their best work and maintain a healthy work-life balance.

Basic Qualifications:* PhD, or Master's degree with 10+ years of experience in Computer Science, ML, or related field
* 5+ years of experience building and deploying ML models for business applications, including edge AI optimization
* Expertise in deep learning algorithms, particularly in computer vision and sensor fusion
* In-depth knowledge of ML paradigms including traditional ML, deep learning, LLMs, and vision transformers
* Proficiency in Python and C/C++ for ML development and low-level optimization
* Experience with ML frameworks such as PyTorch or TensorFlow
* Strong understanding of camera and sensor principles and operations

Preferred Qualifications:* Experience with ML model optimization tools for edge inference, e.g. TFLite or TVM
* Knowledge of ML-specific hardware architectures (e.g., TPUs, NPUs)
* Familiarity with hardware description languages (e.g., VHDL or Verilog) and/or CUDA for GPU acceleration
* Publications in conferences/journals such as CVPR, ICCV, NeurIPS, ICML, or IEEE TPAMI
* Track record of patents in relevant fields
* Hardware-software co-design experience for ML acceleration
* Strong communication skills and ability to work in a fast-paced, collaborative environment