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Director, Applied Science (Amazon AGI)
Shankar Ananthakrishnan has 20+ years of hands-on and technical leadership experience in AI spanning core foundation model development for Amazon Nova, as well as Conversational AI applications such as Alexa. Shankar received his B.Eng. in Electronic & Telecommunication Engineering from the University of Mumbai, India, and his M.Sc. and Ph.D. degrees, both in Electrical Engineering, from the University of Southern California. He is currently a Director of Applied Science at Amazon AGI, leading pre-training, post-training, and AI Safety efforts for Amazon Nova foundation models. Previously, Shankar led a range of Natural Language Understanding (NLU) efforts for Amazon Alexa, and Machine Translation initiatives at Raytheon BBN Technologies. Shankar's research interests are broadly centered around foundation model development (pre- and post-training), speech recognition, statistical machine translation of text and speech, natural language understanding, and related areas in statistical pattern recognition and machine learning. He has published over 50 papers in peer-reviewed conferences and journals, and is the recipient of best paper awards at leading conferences, including ICASSP 2005 and Interspeech 2010. Specialties: foundation models & LLMs, speech recognition, machine translation, natural language understanding. Publications: https://scholar.google.com/citations?user=Xd-eo3AAAAAJ&hl=en
University of Southern California
Ph.D., Electrical Engineering
N/A – Present
University of Southern California
M.Sc., Electrical Engineering
N/A – Present
University of Mumbai
B.Eng., Electronic & Telecommunication Engineering
N/A – Present
Amazon
Director, Applied Science (Amazon AGI)
April 1, 2021 – June 1, 2026
Amazon
Sr. Manager, Applied Science (Alexa AI)
October 1, 2017 – March 1, 2021
Amazon
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Greater Boston Area
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AT&T Labs - Research
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May 1, 2005 – August 1, 2005
University of Southern California
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August 1, 2002 – May 1, 2008
Cultural Fit Analysis
The candidate has a long tenure at Amazon, progressing through several senior Applied Science roles, which suggests a strong fit within large, fast-paced, and innovation-driven tech environments. Their involvement in open-source projects (AlexaTM 20B) and active publication in academic conferences indicates a collaborative and knowledge-sharing mindset. However, the target role of 'Data Analyst' is a significant mismatch with their extensive experience in Applied Science, Machine Learning, and leadership in AI/ML R&D. Their background is far more aligned with a Principal/Distinguished Scientist or Director-level role in AI/ML.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership, strategic thinking, and the ability to drive complex R&D initiatives. Their experience in managing diverse teams and leading significant projects (e.g., AlexaTM 20B, Nova models) indicates excellent operational fit for senior roles requiring both technical depth and managerial acumen. The descriptions suggest a proactive approach to innovation and problem-solving.