Applied Scientist, Conversational AI ModEling and Learning

Job title:

Applied Scientist, Conversational AI ModEling and Learning

Company

Amazon

Job description

DESCRIPTIONConversational AI ModEling and Learning (CAMEL) team is part of Amazon Devices organization where our mission is to build 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 a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems 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 strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions.Key job responsibilities
As an Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Prompt Engineering and Optimization, Supervised Fine-Tuning, Learning from Human Feedback, Evaluation, Self-Learning, etc. Your work will directly impact our customers in the form of novel products and services.BASIC QUALIFICATIONS– PhD, or Master’s degree and 2+ years of building machine learning models for business application experience
– PhD, or a Master’s degree and experience in CS, CE, ML or related field research
– Experience programming in Java, C++, Python or related language
– 3+ years’ experience with modeling languages and tools like PyTorch / TensorFlow, R, scikit-learn, numpy, scipy, etc
– Solid ML background and familiar with standard NLU, NLG, and LLM techniquesPREFERRED QUALIFICATIONS– PhD in Computer Sciences, Electrical Engineering, or Mathematics with specialization in machine learning, deep learning, or natural language processing
– 1+ 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.
– 2+ years experience with large scale distributed systems such as Hadoop, Spark etc.
– Excellent written and spoken communication skillsAmazon 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, 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.

Expected salary

Location

Toronto, ON

Job date

Sun, 15 Dec 2024 06:54:12 GMT

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