Applied Scientist, Amazon Prime, Prime AI/ML Science
Confirmed live in the last 24 hours
Amazon.com Services LLC
Job Description
There are numerous scientific and technical challenges you will get to tackle in this role, such as global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, GenAI based content creation, foundation modeling, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from GenAI, LLMs, deep learning, supervised learning, bandits, optimization, and RL.
As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of.
You will also utilize and be exposed to the latest in AI/ML technologies and infrastructure: AWS technologies (EMR/Spark, Redshift, Sagemaker, DynamoDB, S3, ...), various AI/ML algorithms and techniques (GenAI, LLMs, transformers, sequential models, Neural Networks, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques.
Major responsibilities
- Build and develop AI and machine learning models and supporting infrastructure at TB scale, in coordination with software engineering teams.
- Leverage GenAI, LLMs, deep learning, reinforcement learning for building production AI/ML systems
- Develop backtesting/offline policy estimation tools and integrate with reporting systems.
- Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.
- Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes.
- Work closely with the business to understand their problem space, identify the opportunities and formulate the problems.
- Use AI, machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems.
- Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.
Basic Qualifications
- Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field- Experience programming in Java, C++, Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
Preferred Qualifications
- Experience implementing algorithms using both toolkits and self-developed code- Have publications at top-tier peer-reviewed conferences or journals
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at pythonjavagoawsmachine learningaidataproductdesign
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