Amazon-posted 3 months ago
$150,400 - $260,000/Yr
Senior
Sunnyvale, CA

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 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.

  • Leverage technical expertise and experience to collaborate with other applied scientists and engineers.
  • Research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations.
  • Analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources.
  • Work on core LLM technologies, including Supervised Fine-Tuning (SFT), In-Context Learning (ICL), Learning from Human Feedback (LHF).
  • Impact customers through the development of novel products and services.
  • 3+ years of building machine learning models for business application experience.
  • PhD, or Master's degree and 6+ years of applied research experience.
  • Experience programming in Java, C++, Python or related language.
  • Experience with neural deep learning methods and machine learning.
  • Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy.
  • Experience with large scale distributed systems such as Hadoop, Spark.
  • Full range of medical, financial, and/or other benefits.
  • Equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package.
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