DESCRIPTION

Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Artificial General Intelligence (AGI) organization where our mission is to create a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints.

We are looking for talented and experienced science leader in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP) and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have solid technical background and extensive experience in leading projects and technical teams. The ideal candidate would also have experiences in developing natural language processing systems (particularly LLM based systems) for industry applications, enjoy operating in highly dynamic and ambiguous environments, be self-motivated to take on challenging problems to deliver customer impact.

Key job responsibilities
As a Senior Applied Scientist, you will:

* Build a strong and coherent team with particular focus on sciences and innovations in LLM technologies for conversation AI applications
* Serve as a technical lead on demanding and cross-team projects, and effectively collaborating with multiple cross-organizational teams
* Apply technical influence on partner teams, increasing their productivity by sharing your deep knowledge

BASIC QUALIFICATIONS

– PhD degree with 3+ years of applied science experience for industry business applications or Master degree with 6+ years of applied science experience for industry business applications
– 5+ years of programming experience in Python, Java, or related languages.
– 5+ years’ experience with modeling languages and tools like PyTorch / TensorFlow, R, scikit-learn, numpy, scipy, etc.
– Graduate degree (MS or PhD) in Computer Science, Electrical Engineering, Mathematics, or a related field.
– Solid ML background and familiar with standard NLU, NLG, and LLM techniques

PREFERRED QUALIFICATIONS

– PhD in Computer Sciences, Electrical Engineering, or Mathematics with specialization in machine learning, deep learning, or natural language processing
– 4+ years experience in building conversational AI and/or natural language processing systems.
– Publications at peer-reviewed NLP/ML conferences (e.g. ACL, EMNLP, NAACL, NeurIPS, ICLR, ICML, AAAI, etc.)
– Scientific thinking and the ability to invent, a track record of thought leadership and contributions that have advanced the field.
– 5+ years experience with large scale distributed systems such as Hadoop, Spark etc.
– Excellent written and spoken communication skills

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 $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 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, WA, Bellevue