Member of Technical Staff at Prime Intellect
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Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Seasoned Research Engineer + Product engineer working at the frontier of large language model training - reinforcement learning, RL envs and model evaluation infrastructure with broad applied research experience, strong product intuition and a mathematical background. Let's connect.
Birla Institute of Technology and Science, Pilani
Bachelor's degree, Electrical and Electronics Engineering
N/A – Present
UC San Diego
Master’s Degree, Electrical and Computer Engineering
N/A – Present
Prime Intellect
Member of Technical Staff
June 1, 2026 – Present
San Francisco Bay Area
Mercor
Member of Technical Staff
November 1, 2025 – June 1, 2026
San Francisco Bay Area
Amazon
Senior Applied Scientist
February 1, 2024 – November 1, 2025
Amazon
Applied Scientist
February 1, 2021 – February 1, 2024
JPMorgan Chase & Co.
Vice President - Applied Machine Learning
January 1, 2021 – February 1, 2021
New York, New York, United States
JPMorgan Chase & Co.
Senior Associate - Machine Learning and AI
April 1, 2019 – December 1, 2020
New York, New York, United States
Arm
Research Scientist
May 1, 2018 – April 1, 2019
Bosch USA
Deep Learning Intern
June 1, 2017 – September 1, 2017
Cultural Fit Analysis
The candidate has worked across various company types, from large enterprises like Amazon and JPMorgan Chase to AI-focused startups like Mercor and Prime Intellect. This diversity suggests adaptability to different work environments. The consistent focus on advanced ML/AI research and application indicates a strong alignment with a culture valuing innovation and technical depth in this domain. However, the lack of project details makes it hard to assess collaboration or broader impact beyond technical contributions.
Soft Skills & Operational Fit
The candidate's experience descriptions suggest a focus on technical execution and research. Without psychometric or English test results, it is difficult to assess soft skills like communication, teamwork, or stress handling. The operational fit appears strong given the direct relevance of past roles to ML engineering.