Senior Applied Scientist, Amazon Beauty Tech
3 weeks ago
To achieve our vision, we think big and tackle technological challenges every day. We need builders and disruptors who are not afraid to innovate Our architecture and development processes support rapid experimentation, global deployments, and self-service capabilities that allow us to scale better.
We build:
- Amazon scale systems: All our technology needs to work at Amazon scale, serving millions of customers with millisecond-level latency.
- Immersive customer experiences: We will create elevated and immersive customer experiences that using cutting-edge UI-technologies and user-centric design patterns.
- Computer Vision and augmented reality (AR) experiences: We bring exciting experiences directly to the customer's mobile phone using their cameras and combinations of computer vision and AR.
- Personalization using machine learning: We use latest advances in ML and GenAI to provide better-personalized shopping experiences.
- Data & analytics pipelines: Amazon is data-driven, and a robust data backbone is necessary for our systems. We build on core AWS services such as EC2, S3, DynamoDB, SageMaker, StepFunctions, etc.
- Multi-device support: We build for all traditional surfaces - desktop browsers, mobile browsers, and mobile applications.
Key job responsibilities
We are looking for talented and innovation-driven scientists who are passionate about leveraging the latest advances in either a) Generative AI, Diffusion Models, Computer Vision (CV), Image Processing, and related technologies, and/or b) Recommendation Systems (RecSys), Personalization algorithms, LLM-based RecSys, and related technologies, to solve customer problems in the Beauty space. You will have an opportunity to revolutionize the customer shopping experience across the world's most extensive catalog of beauty products. You will be directly responsible for leading the ideation, design, prototyping, development, and launch of innovative scientific solutions that address customer problem in the beauty and shopping space. You will closely partner with product managers, UX designers, engineers, and the broader Amazon scientific community to pioneer state-of-the-art solutions to extremely challenging problems in machine learning and computer vision. You will help hire, mentor, and develop the best and brightest science and engineering talent while our organization rapidly continues to expand. You will be our organization's Tech Evangelist and represent our organization in key internal and external AI, ML, CV or RecSys conferences.
About the team
Amazon Beauty Tech is a key and essential part of the Consumables organization and North America Stores. We are a passionate group of engineers, scientists, product managers, and designers who drive technological innovation to improve the customer shopping experience. We have a startup-like work culture where innovation is encouraged; we are never afraid to propose big ideas for fear of failing
We are open to hiring candidates to work out of one of the following locations:
Vancouver, BC, CAN
BASIC QUALIFICATIONS
- 3+ years of building machine learning models for business application experience- PhD, or Master's degree and 5+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
PREFERRED QUALIFICATIONS
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.- Experience with conducting research in a corporate setting
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Hands-on experience in building solutions for one or both of: a) Generative AI, CV, Diffusion Models; b) Recommendation Systems (RecSys), Personalization, Indexing, ANN, User Behavioral Modeling, Ranking Algorithms, and relevant technologies, hands-on LLM & NLP experience being a strong plus.
- Top tier AI publications is a strong plus.
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