Amazon

DESCRIPTION

At Amazon, we strive every day to be Earth’s most customer-centric company. Selling Partner Support Engagement (SPSE) Science delivers on this by building AI-enhanced experiences that provide world class support to our global network of selling partners. We are building applications at the forefront of GenAI applications, tackling the challenges caused by the volume, diversity, and complexity of our selling partner's needs.

Do you want to join a team of scientists with critical problem solving skills who are innovating on behalf of our customers using statistical inference, natural language processing, computer vision and generative AI? Are you interested in helping us redefine what world class support can be in an age of automation and AI, while prizing human empathy and ingenuity? Are you excited by the prospect of your solutions resulting in large-scale impact while getting a chance to research at the forefront of AI innovation?

The SPSE Science Team is looking for an Applied Scientist to build statistical, machine learning and GenAI solutions (AI agents, LLM fine-tuning) that help us understand and solve our most challenging problems. We need to better understand our Sellers and the problems they face, to augment our human workforce with smarter tools, to anticipate problems so that we are prepared to deal with them and to automatically diagnose and resolve issues. In this role, you will have ownership of the end-to-end development of solutions to complex problems and you will play an integral role in strategic decision-making. You will also work closely with engineers, operations teams and product owners to build ML pipelines, platforms and solutions that solve problems of defect detection, automation, and workforce optimization.

Key job responsibilities

• Use state-of-the-art Machine Learning and Generative AI techniques to create the next generation of tools that empower Amazon's Selling Partners and Support Associates to succeed.
• Design, develop and deploy models that either interact with end users or automate entire workflows.
• Work closely with teams of scientists and software engineers to drive online model implementations with impactful features through A/B testing.
• Establish scalable, efficient, automated processes for large scale data analyses, model benchmarking, model validation and model implementation.
• Research and implement novel machine learning and statistical approaches.
• Participate in strategic initiatives to employ the most recent advances in ML in a fast-paced, experimental environment.

About the team

Selling Partner Support Engagement Science (Titans Science) is a growing team of scientists engaged in the research and development of the next generation of ML-driven technology to empower Amazon's Selling Partners to succeed. We strive to radically simplify the seller experience, making it easy to accomplish critical tasks such as launching new products, understanding and complying with Amazon’s policies and taking actions to grow their business. Scientists on the team get an opportunity to learn the state of the art end-to-end ML tooling and infrastructure offered through AWS and other open source technology stacks. The complexity of our work also give scientists an opportunity to successfully publish research in leading journals and conferences.

We value diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. Additionally, the team fosters an inclusive and learning culture, inspiring us to embrace our uniqueness. We highly value individual growth; you will also find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

BASIC QUALIFICATIONS

• 3+ years of building models for business application experience
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience programming in Java, C++, Python or related language
• Experience in solving business problems through machine learning, data mining and statistical algorithms
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

PREFERRED QUALIFICATIONS

• Experience with generative deep learning models applicable to the creation of synthetic humans like CNNs, GANs, VAEs and NF
• Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
• Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)

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 $136,000/year in our lowest geographic market up to $223,400/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.