Job Description
Job Description
The Sr. Applied Scientist will develop scientific algorithms, and deploy Large Language Model (LLM) solutions to improve the customer experience through chatbot. The Sr. Applied Scientist will work closely with technology and product teams to solve business and technology problems using scientific approaches and genAI to build new services that surprise and delight our customers. As a Sr. Applied Scientist at Amazon you will apply scientific principles to support significant invention, develop code, and will be deeply involved in bringing their algorithms to production. You will also work on cross-disciplinary efforts with other scientists within Amazon.
Key job responsibilities
- Drive actions at scale to provide and deploy efficient solutions for customers using scientifically-based methods, LLM and decision making.
- Help to support production systems that take inputs from multiple models and make decisions in real time.
- Automate feedback loops for algorithms in production.
- Utilize Amazon systems and tools to effectively work with terabytes of data, and ensure the quality of input data.
A day in the life
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!
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:
1. Medical, Dental, and Vision Coverage
2. Maternity and Parental Leave Options
3. Paid Time Off (PTO)
4. 401(k) Plan
About the team
The Shipping Delivery and Support (SDS) Tech team’s mission is to enable successful deliveries through fast and efficient support experiences for our drivers, recipients, and associates. The science team utilizes advanced scientific methods, including LLM, to support the business, improve driver support quality, and become more efficient with deliveries.
BASIC QUALIFICATIONS- PhD, or Master's degree and 6+ years of applied research experience
- 3+ years of building machine learning models for business application 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 large scale distributed systems such as Hadoop, Spark etc.
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 -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 $150,400/year in our lowest geographic market up to $260,000/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 -benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
Job Tags
Full time, Temporary work, Seasonal work,