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Member of Technical Staff @Anthropic
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Stanford University
Master of Science (MS), Computer Science
January 1, 2016 – January 1, 2017
Stanford University
Bachelor of Science (B.S.), Computer Science
January 1, 2012 – January 1, 2017
Anthropic
Member of Technical Staff
October 1, 2024 – Present
PhenomeX
Staff Software Engineer, Machine Learning
March 1, 2023 – May 1, 2024
Emeryville, California, United States
PhenomeX
Senior Software Engineer, Machine Learning
October 1, 2020 – March 1, 2023
Emeryville, California, United States
NVIDIA
Senior Deep Learning Engineer
March 1, 2018 – October 1, 2020
Santa Clara, California, United States
Stanford University
Course Assistant - Artificial Intelligence (CS221)
September 1, 2017 – December 1, 2017
Berkeley Lights, Inc.
Intern - Research Associate Scientist
June 1, 2017 – September 1, 2017
Emeryville, California, United States
Stanford University
Course Assistant - Reinforcement Learning (CS234)
April 1, 2017 – June 1, 2017
Stanford University
Course Assistant - Artificial Intelligence (CS221)
September 1, 2016 – January 1, 2017
Stanford, California
Q-Games
Programming Intern
June 1, 2015 – August 1, 2015
Kyoto, Japan
Cultural Fit Analysis
The candidate has worked at prominent tech companies (NVIDIA, Anthropic) and biotech firms (PhenomeX/Berkeley Lights), demonstrating adaptability across different industry contexts. Their experience as a Course Assistant at Stanford also indicates a willingness to mentor and collaborate. The diversity of ML applications (physical design acceleration, cellular process analysis) suggests a broad interest in problem-solving. However, without specific project details or team interaction insights, a deeper cultural fit analysis is not possible.
Soft Skills & Operational Fit
The candidate's resume highlights experience in guiding development and accelerating iteration, suggesting strong leadership and operational skills. The description of designing and deploying ML infrastructure indicates a proactive approach to improving system efficiency and reliability. However, without psychometric test results or interview data, a comprehensive assessment of soft skills and operational fit is limited.