DESCRIPTION

At OpsTech Solutions (OTS), we are a technology centric services organization that designs, builds, and sustains the invisible, high-quality network, compute infrastructure and device scaffolding that empowers and protects Amazon’s global Operations.

The OTS DataTech team drives enterprise data strategy and support across OTS. Our charter encompasses OTS-wide efforts, including AI/ML capability to fuel innovation and automation for OTS.

We are looking for a passionate, talented, and innovative Applied Scientist with a background in developing and implementing state-of-the-art Generative AI (GenAI) solutions. In this role, you will play a pivotal role in shaping the vision, roadmap, design, development and implementation of science and software based solutions from beginning to end.

Key job responsibilities
As an Applied Scientist in the DataTech team, you will build foundational GenAI components that will enable our customers to build GenAI applications for their use cases across OTS. You will enable the seamless integration of scientific products with new and existing systems, ultimately leading to increased operational efficiency and productivity across OTS.

You will also work on projects involving supervised and unsupervised learning, NLP, and more.

Come join OTS DataTech as we continue to innovate and pioneer the AI/ML space within OTS!

A day in the life
Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment.

The benefits that generally apply to regular, full-time employees include:
– Medical, Dental, and Vision Coverage
– Maternity and Parental Leave Options
– Paid Time Off (PTO)
– 401(k) Plan  

If you are not sure that every qualification on the list above describes you exactly, we’d still love to hear from you!

At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

BASIC QUALIFICATIONS

– MS or PhD in quantitative field (CS, CE, ML preferred) or equivalent relevant work experience.
– Strong background in machine learning, including supervised and unsupervised learning algorithms.
– Experience developing, building and implementing complex software systems, especially involving ML, that have been successfully delivered to customers.
– Knowledge of Generative AI (GenAI) and its applications.
– Proficiency in programming languages such as Python, Java, or C++.
– Strong communication skills, both written and verbal.

PREFERRED QUALIFICATIONS

– Experience with fine-tuning and deploying Large Language Models (LLMs) for customer facing applications.
– Knowledge of RAG and its applications.
– Experience with AWS technologies.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

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 $222,200/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.

USA, TX, Austin