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Description

The Learning & Development Science team in Amazon Logistics (AMZL) builds state-of-the-art Artificial Intelligence (AI) solutions for enhancing leadership and associate development within the organization. We develop technology and mechanisms to map the learner journeys, answer real-time questions through chat assistants, and drive the right interventions at the right time. As an Applied Scientist on the team, you will play a critical role in driving the design, research, and development of these science initiatives.

The ideal candidate will lead the research on learning and development trends, and develop impactful learning journey roadmap that align with organizational goals and priorities. By parsing the information of different learning courses, they will utilize the latest advances in Gen AI technology to address the personalized questions in real-time from the leadership and associates through chat assistants. Post the learning interventions, the candidate will apply causal inference or A/B experimentation frameworks to assess the associated impact of these learning programs on associate performance. As a part of this role, this candidate will collaborate with a large team of experts in the field and move the state of learning experience research forward. They should have the ability to communicate the science insights effectively to both technical and non-technical audiences.

Key job responsibilities

* Apply science models to extract actionable information from learning feedback

* Leverage GenAI/Large Language Model (LLM) technology for scaling and automating learning experience workflows

* Design and implement metrics to evaluate the effectiveness of AI models

* Present deep dives and analysis to both technical and non-technical stakeholders, ensuring clarity and understanding and influencing business partners

* Perform statistical analysis and statistical tests including hypothesis testing and A/B testing

* Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation

Basic Qualifications

- PhD in computer science, computer engineering, or related field

- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning

- Experience programming in Java, C++, Python or related language

- 1+ years of CS, CE, ML or related field experience

Preferred Qualifications

- Experience implementing algorithms using both toolkits and self-developed code

- Have publications at top-tier peer-reviewed conferences or journals

- Hands on experience building models with deep learning frameworks like MXNet, Tensorflow, or PyTorch

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.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $129,400/year in our lowest geographic market up to $212,800/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 https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.