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solving intelligence at tesla
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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
Carnegie Mellon University
Senior Thesis in Machine Learning
May 1, 2017 – May 1, 2018
Greater Pittsburgh Area
Microsoft
Software Engineer Intern
May 1, 2017 – August 1, 2017
Redmond, WA
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.