Description

The Amazon Smart Vehicles (ASV) science team is seeking a passionate and skilled Applied Scientist with extensive expertise in advanced LLM technologies.

This role involves innovating in rapidly evolving areas of AI research, focusing on creating personalized services to enhance drivers' and passengers' experiences. Your work will aim to simplify their lives, keep them informed, entertained, productive, and safe on the road, with direct application to prominent Amazon products.

If you have extensive expertise in LLMs, natural language processing, and machine learning, along with experience in high-performing research teams, this could be the perfect opportunity for you. Our dynamic and fast-paced environment demands a high level of independence in decision-making and the ability to drive ambitious research initiatives through to production. You will collaborate closely with other science and engineering teams, as well as business stakeholders, to ensure your contributions are both impactful and delivered with maximum efficiency.

Key job responsibilities

• Leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI)

• Work with talented peers to lead the development of novel algorithms and modeling techniques to advance the state of the art with LLMs

• Collaborate with other science and engineering teams as well as business stakeholders to maximize the velocity and impact of your contributions

About the team

This is an exciting moment to lead in AI research. As part of the Amazon Smart Vehicles science team, you have the opportunity to shape the future by enhancing information-driven experiences for Amazon customers around the globe. Your work will directly influence customers through innovative products and services powered by language and multimodal technology!

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

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

Preferred Qualifications

• 1. Experience with modeling tools such as PyTorch, SageMaker, R, scikit-learn, Tensorflow, numpy, scipy etc.
• 2. PhD in math/statistics/engineering or other equivalent quantitative discipline
• 3. Publications at peer-reviewed NLP/ML conferences (e.g. ACL, EMNLP, NAACL, COLING, NeurIPS, ICLR, ICML, AAAI, etc.)

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.

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.