Alex Kuefler
ML EngineerSr. Research Scientist at Waymo
Waymo
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Sr. Research Scientist at Waymo
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’s Degree, Symbolic Systems
January 1, 2015 – January 1, 2017
Occidental College
Bachelor’s Degree, Major: Cognitive Science, Minor: Computer Science
January 1, 2012 – January 1, 2015
Waymo
Senior Research Scientist
April 1, 2020 – Present
San Francisco Bay Area
Element AI
Research Engineer
May 1, 2019 – April 1, 2020
London Area, United Kingdom
OSARO
Deep Learning Engineer
October 1, 2017 – April 1, 2019
San Francisco Bay Area
Stanford University
Graduate Research Assistant
June 1, 2016 – September 1, 2017
Datmo
Machine Learning Intern
March 1, 2016 – June 1, 2016
Google Summer of Code, Student Open Source Developer
May 1, 2015 – September 1, 2015
Carnegie Mellon University
Undergraduate Researcher
May 1, 2014 – August 1, 2014
Greater Pittsburgh Region
JPL (NASA's Jet Propulsion Laboratory)
Student Intern
September 1, 2013 – January 1, 2014
Pasadena, California
Occidental College
Research Assistant
May 1, 2013 – August 1, 2013
Los Angeles
Deep View Morphing (CS 231A course project)
April 1, 2016 – June 1, 2016
View morphing takes two views of a 3D object and infers the appearance of the object at intermediary views. This problem is traditionally approached through the lens of epipolar geometry, but such techniques require computing pixel correspondences, and are brittle to changes in visibility. This work introduces a neural network based approach for learning to generate morphs of 3D objects. Three experiments are performed to assess the model’s ability to reconstruct images and generalize to unseen objects.
Attentional Scene Classification with Human Eye Movements (CS 231N course project)
January 1, 2016 – April 1, 2016
Attentional models of computer vision present a biologically plausible and computationally inexpensive alternative to the deep learning pipeline popularized for image recognition in recent years. Although drawing inspiration from perceptual psychology, little research has attempted to leverage attentional policies inferred from human data in order to improve image classification for artificial intelligence. The purpose of this work is to compare the performance of a sequential classification model that learns a multiclass scene classification task, after training on sequences of observations generated by humans, saliency-maps, and reinforcement learning.
Cultural Fit Analysis
The candidate demonstrates a strong research-oriented background with experience in both academic and industry settings (Waymo, Element AI, OSARO). The diversity of projects, from scene classification to view morphing and autonomous vehicle behavior prediction, indicates adaptability and a broad interest in ML applications. The continuous engagement in cutting-edge research and development roles suggests a fit for innovative and challenging environments. However, the lack of explicit team collaboration or leadership roles in descriptions makes it difficult to fully assess cultural fit beyond technical contributions.
Soft Skills & Operational Fit
The candidate's extensive research and project experience suggest strong problem-solving, analytical thinking, and independent work capabilities. Collaboration is implied through co-authored publications and team-based projects, though specific soft skill assessments are not available.