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AI/ML Engineer @ General Motors | Machine Learning, Artificial Intelligence
Results-oriented ML Engineering Manager / Tech Lead with a successful track record in leading and scaling high-performing teams with expertise in building, evaluating, and optimizing ML models for perception, prediction and planning within the AV stack. Proven ability to collaborate cross-functionally, influencing product roadmaps, and delivering impactful solutions to improve model performance, and enhance developer productivity. Adept at supporting team members’ career growth with a track record of significant promotions.
University of Southern California
Master of Science (MS), Electrical Engineering
January 1, 2010 – January 1, 2012
Vellore Institute of Technology
Bachelor of Technology (B.Tech.), Electronics & Communication Engineering
January 1, 2006 – January 1, 2010
General Motors
AI/ML Engineer
April 1, 2024 – Present
Mountain View, California, United States · Hybrid
Cruise
Engineering Manager, ML - Behavior Performance
December 1, 2021 – March 1, 2024
San Francisco Bay Area
Cruise
Senior Machine Learning Engineer / Tech Lead
June 1, 2019 – December 1, 2021
San Francisco Bay Area
Cruise
Senior Machine Learning Engineer
June 1, 2017 – June 1, 2019
San Francisco Bay Area
Allyke, Inc.
Computer Vision Engineer
January 1, 2016 – June 1, 2017
Greater Boston Area
Vecna Technologies
Computer Vision Engineer
July 1, 2012 – January 1, 2016
Cambridge, MA
Qualcomm Research
Interim Engineering Intern
January 1, 2012 – April 1, 2012
Greater San Diego Area
Sony Corporation
R&D Engineer Intern
June 1, 2011 – August 1, 2011
Tokyo, Japan
Cedars-Sinai Medical Center
Volunteer Research Assistant
November 1, 2010 – December 1, 2011
Greater Los Angeles Area
Cultural Fit Analysis
The candidate's career trajectory shows a strong focus on AI/ML and Computer Vision across various industries (automotive, retail, medical, robotics). Their experience at Cruise, moving from Senior ML Engineer to Engineering Manager, indicates growth and leadership within a fast-paced, innovative environment. The diversity of projects (autonomous vehicles, visual discovery, medical screening, robotics) suggests adaptability and a broad interest in applying technical skills to different challenges. However, the target role of 'Data Analyst' represents a significant pivot from their established career path in ML engineering and management. While their background implies strong analytical capabilities, the specific cultural fit for a dedicated data analyst role, which often involves different tools, methodologies, and stakeholder interactions compared to ML engineering, is not fully evident from the provided data. Their experience is heavily skewed towards model development and deployment rather than traditional data analysis, reporting, and dashboarding.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership, communication, and problem-solving skills through their experience descriptions. Their management roles highlight abilities in team building, mentorship, cross-functional collaboration, and process establishment. The detailed descriptions of impact (e.g., productivity improvements, disengagement reduction) suggest a results-oriented approach. However, the target role is 'Data Analyst', which is a significant shift from their extensive ML Engineering and Management background. While analytical skills are inherent in ML, the specific focus on data analysis tools, reporting, and business intelligence, which are typical for a Data Analyst, is not explicitly detailed in their experience. This suggests a potential mismatch in operational fit for a pure Data Analyst role without further validation of their specific data analysis capabilities.