Sudeep Pillai
Computer VisionAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Founder / CEO at VLM Run | ML Leader | MIT PhD ‘17 (CS)
Building VLM Run (https://vlm.run) - a unified gateway for Visual AI. Previously, I led the ML Engineering team at Toyota Research Institute (TRI) that was responsible for the design and development of all ML models deployed into Toyota’s next-generation L4 autonomous vehicle fleet. I spent my initial tenure at TRI as a research scientist in the ML team, spearheading their large-scale self-supervised learning efforts for autonomous driving. I received my PhD in CS from MIT where I focused on self-supervised perception and learning in SLAM-aware mobile robots. My thesis made key contributions in monocular SLAM supported 3D object recognition, self-supervised visual ego-motion learning, and self-supervised visual place recognition in spatially-cognizant mobile robots. I also spent a summer interning at Mitsubishi Electric Research Labs (MERL) developing high-performance 3D stereo reconstruction techniques. CV: http://people.csail.mit.edu/spillai/data/sudeep-pillai-cv.pdf For a more details of my work, see: http://spillai.org
Massachusetts Institute of Technology
Master of Science (M.S.), Computer Science
January 1, 2011 – January 1, 2014
Massachusetts Institute of Technology
Doctor of Philosophy (Ph.D.), Computer Science (ML/CV/Robotics)
January 1, 2011 – January 1, 2017
University of California, Berkeley
Summer Sessions
May 1, 2006 – July 1, 2006
University of Michigan
B.S., Mechanical Engineering
January 1, 2005 – January 1, 2008
Sharjah Indian School
High school diploma
January 1, 1993 – January 1, 2005
VLM Run
Founder / CEO
March 1, 2024 – Present
San Francisco Bay Area · On-site
South Park Commons
Founder Fellow
September 1, 2023 – September 1, 2024
San Francisco, CA · On-site
On Deck
On Deck Deep Tech Fellow | ODDT1
October 1, 2021 – September 1, 2022
San Francisco Bay Area
Toyota Research Institute
Manager and Team Lead, Machine Learning Engineering
June 1, 2019 – April 1, 2021
Toyota Research Institute
Research Scientist (Machine Learning) and Tech. Lead
October 1, 2017 – June 1, 2019
MERL
Research Intern
May 1, 2014 – August 1, 2014
Cambridge, MA
MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)
Graduate Student
September 1, 2011 – August 1, 2017
Greater Boston Area
PhaseSpace Inc.
Computer Vision Developer
May 1, 2009 – May 1, 2011
San Leandro, CA
Segway
R&D Engineer Intern
May 1, 2008 – August 1, 2008
Bedford, NH
Envoy Project
June 1, 2010 – June 1, 2013
A robot assistant that can help people in indoor (multi-floor environments) by interacting and guiding them to their destinations. Capable of asking for assistance when required (e.g. calling elevators).
Cultural Fit Analysis
The candidate's background, spanning academic research at MIT, industry leadership at Toyota Research Institute, and entrepreneurial ventures, demonstrates adaptability and a drive for innovation. Their work on autonomous vehicles and visual AI infrastructure aligns well with cutting-edge technology environments. The diversity of their experience, from fundamental research to product deployment and startup founding, suggests a strong cultural fit for organizations that value both deep technical expertise and practical application.
Soft Skills & Operational Fit
The candidate's career trajectory, including founding a company and leading teams, suggests strong leadership, problem-solving, and strategic thinking. Their involvement in fellowships (South Park Commons, On Deck Deep Tech) indicates a proactive approach to networking and staying current with industry trends. The descriptions of their roles at Toyota Research Institute highlight an ability to manage complex technical projects and deploy advanced ML models, implying strong operational fit for high-impact roles.