Senior Research Scientist, Conversational AI ModEling and Learning

$143,300 - $247,600/Yr

Amazon - Sunnyvale, CA

Full-time - Senior

Sunnyvale, CA

10,001+ employees

Sporting Goods, Hobby, Musical Instrument, Book, and Miscellaneous Retailers

About the position

The Conversational AI Modeling and Learning (CAMEL) team is a part of Amazon's Artificial General Intelligence (AGI) organization, dedicated to creating a best-in-class Conversational AI that is intuitive, intelligent, and responsive. Our mission involves developing superior Large Language Models (LLM) solutions and services that enhance the capabilities built into the model, enabling the utilization of thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are seeking a passionate, talented, and resourceful Senior Research Scientist specializing in LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems, and/or Information Retrieval. The ideal candidate will have a strong machine learning background and a desire to push the envelope in these areas. Hands-on experience in building Generative AI solutions with LLMs is essential, along with a self-motivated approach to tackling challenging problems that deliver significant customer impact. The role requires moving quickly to ship solutions and iterating based on user feedback and interactions. As a Senior Research Scientist, you will leverage your technical expertise and experience to demonstrate leadership in addressing large, complex problems. You will set the direction and collaborate with other talented applied scientists and engineers to research and develop LLM modeling and engineering techniques that reduce friction and enable natural and contextual conversations. Your responsibilities will include analyzing, understanding, and improving 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, Model Fine-Tuning, Reinforcement Learning from Human Feedback (RLHF), and Evaluation. Your work will have a direct impact on our customers through the development of novel products and services.

Responsibilities

• Leverage technical expertise to tackle large complex problems in LLM and AI.
• Collaborate with applied scientists and engineers to research and develop LLM modeling techniques.
• Analyze and improve user experiences using Amazon's data sources and computing resources.
• Work on core LLM technologies including Prompt Engineering and Model Fine-Tuning.
• Implement Reinforcement Learning from Human Feedback (RLHF) and Evaluation techniques.
• Deliver impactful solutions that enhance customer interactions with AI.

Requirements

• PhD degree with 3+ years of applied science experience or Master's degree with 6+ years of applied science experience in industry applications.
• 5+ years of programming experience in Python, Java, C/C++, or related languages.
• 5+ years' experience with modeling languages and tools like PyTorch, TensorFlow, R, scikit-learn, numpy, scipy.
• Graduate degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
• Solid machine learning background and familiarity with standard NLU, NLG, and LLM techniques.

Nice-to-haves

• PhD in Computer Sciences, Electrical Engineering, or Mathematics with specialization in machine learning or NLP.
• 4+ years experience in building conversational AI and/or NLP systems.
• Publications at peer-reviewed NLP/ML conferences (e.g., ACL, EMNLP, NAACL, NeurIPS, ICLR, ICML, AAAI).
• Track record of thought leadership and contributions advancing the field.
• 5+ years experience with large scale distributed systems such as Hadoop or Spark.
• Excellent written and spoken communication skills.

Benefits

• Medical insurance coverage
• Financial benefits including equity and sign-on payments
• Comprehensive employee benefits package
• Diversity and inclusion programs
• Flexible work arrangements