R&D Analyst
Sep 2025 – PresentStealth Startup
- Collected, analyzed, and labeled video-based data used in AI and robotics research and development initiatives.
- Supported R&D operators by clarifying task guidelines, sharing best practices, and assisting with live learning as workflows evolved.
- Tested and validated AI-driven hardware and software systems, identifying functional issues and edge cases during live operation.
- Performed advanced troubleshooting for recurring and non-recurring issues, helping operators resolve blockers and maintain productivity.
- Documented common issues, resolutions, and process improvements to improve consistency and reduce repeat errors.
- Collaborated with engineers to communicate observations, provide product feedback, and support iterative development cycles.
- Contributed high-quality data to live learning models, directly supporting model accuracy and system performance.
- Assisted in drafting and refining procedural documentation to support quality assurance and operational alignment.
- Conduct research and development analysis on data collected from AI-integrated robots, ensuring accuracy and usability for machine learning applications.
- Collect, label, transform, and visualize raw data to support machine learning engineers in training and refining in-house AI models.
- Audit and validate data prepared by collection teams, improving the accuracy and consistency of datasets used for backend and internal AI software.
- Utilize specialized AI-integrated tools to label complex datasets and convert raw data into actionable insights.
- Leverage strong technical skills in Microsoft Office, Microsoft Teams, Slack and Google Workspace to support cross-functional collaboration.
- Maintain working knowledge of AI-driven technologies and robotic systems, including humanoid and task-specific robots.
- Troubleshoot, problem-solve, and support continuous improvements in data handling processes for AI training models.
- Partnered with cross-functional teams to refine data pipelines, streamline labeling workflows, and enhance reporting efficiency, improving data quality and accelerating model training.