San Francisco Bay Area
• Program/Operate numerous FDM systems in a high-volume environment • Operate, control, and optimize multiple Additive Manufacturing processes from CAD file to finished part • Perform all functions related to machine turnaround and preparation • Perform quality inspection on printed parts • Ability to repeatedly produce complex and tight tolerance components • Proficient in AutoDesk Fusion 360
• Managed 30+ projects through AGILE software development cycle. • Design annotation ontology with Autopilot Directors and Engineers. • Amass and audit source data for entire Tesla fleet for Neural Net Models. • Partner with Data Annotation Leadership to set goals, forecast timelines, while ensuring targets are met. • Collaborate with engineers to develop new tooling annotation features. • Isolate process inefficiencies and improve workflows with Autopilot Leadership, Project Management, and Engineering teams. • Road test project features on Tesla fleet and evaluate areas of improvement that are lacking in Neural Net Model.
• Developed and mentored core organization of 55 analysts to train Autopilot AI’s Deep Neural Network. • Mentored core Lead Autopilot Analysts into future roles with growth opportunities in Tesla. • Subject matter expert in workflows (Labeling, QA, and Ontology) for Neural Net Model. • Trained team to meet strict standards via knowledge tests (quizzes, sync manuals, and training resources). • Detect, solve, and escalate all workflow constraints. • Drive lean manufacturing (Kaizen, Six Sigma, Push/Pull, etc.) ideas with leads and analysts. • Hired and expanded team to 600+ to meet goals set by AP Leadership for the Autopilot Analyst team.
• Triaged and flagged field collisions regardless of control state and how Tesla could potentially mitigate and make the car overall safer. • Assisted in saving Tesla $115 Million from NHTSA probe for emergency vehicle collisions by helping train NN models to detect emergency lights on first responder vehicles. • Use Python to dive into databases to filter data, identify where new features could be implemented, verify where AI models could be improved, and escalated scenarios to appropriate engineers to improve Autopilot Safety. • Ran evaluation tests to verify improvements and compare new Neural Models against In-Car Model as well as set of field testing to ensure performance.
• Scheduled maintenance, brake service, air conditioning, tire/wheel balancing, flat tire repair, compliance repair, used car inspection, engine maintenance/repair, transmission/drive train, timing belt, oil change, minor tune ups, major tune ups, OBD I/OBD II troubleshooting, engine pressure, clutch, suspension, and more.