
solving intelligence at tesla
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Building robust embedded software systems and high-throughput cloud data infrastructure.
Carnegie Mellon University
Bachelor’s Degree, Computer Science;
January 1, 2014 – January 1, 2018
Tesla
Staff Software Engineer, Autopilot
August 1, 2019 – Present
San Francisco Bay Area · On-site
NVIDIA
Systems Software Engineer
July 1, 2018 – August 1, 2019
San Francisco Bay Area
Microsoft
Software Engineer Intern
May 1, 2017 – August 1, 2017
Redmond, WA
Carnegie Mellon University
Senior Thesis in Machine Learning
May 1, 2017 – May 1, 2018
Greater Pittsburgh Area
Carnegie Mellon University
Research Assistant at Field Robotics Center
May 1, 2016 – May 1, 2018
Greater Pittsburgh Area
Cultural Fit Analysis
The candidate's experience is heavily skewed towards software engineering, machine learning engineering, and robotics, with significant contributions in C++ and large-scale data infrastructure. While there's exposure to 'Anomaly detection with Azure ML', the overall profile is not a direct fit for a 'Data Analyst' role, which typically focuses more on statistical analysis, reporting, and business intelligence rather than system-level engineering or ML model development. The breadth of skills is high, but the alignment with the target role is low.
Soft Skills & Operational Fit
The candidate's experience descriptions suggest a strong problem-solving ability and a capacity to work on complex, large-scale systems. The roles at Tesla and NVIDIA indicate a high degree of operational responsibility and technical leadership. However, specific soft skill assessments are not available.