AI and ML Engineer

Seeed Studio

Building developer-focused AI products
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
AI + ML Engineer
User-Centered Research
Market Research
Product Design
UI/UX
Prototyping
User Testing
React
Figma
Python
TensorFlow
OAuth 2.0
REST APIs
User-Centered Research
Market Research
Product Design
UI/UX
Prototyping
User Testing
React
Figma
Python
TensorFlow
OAuth 2.0
REST APIs

Skills

User-Centered Research Market Research UI/UX p5.js Node.js React Python Open Interpreter Node-RED TensorFlow REST APIs

Category

IoT / Tech

Awards

During Summer 2024, I worked at Seeed Studio's headquarters in Shenzhen, China, as an AI/ML Engineer within their Industrial Application Group (IAG). My primary focus was advancing the launch of the SenseCAP Watcher, a cutting-edge IoT device. I developed and fine-tuned image recognition models trained on a dataset of over 10,000 images to enable accurate environmental monitoring and anomaly detection.

Leveraging Node-RED, I built seamless integrations between the SenseCAP Watcher and platforms such as Discord, MongoDB, and WhatsApp, enhancing its real-time connectivity. To ensure ease of adoption, I authored comprehensive developer Wiki pages, detailing step-by-step implementation guides for these integrations.

Beyond technical contributions, I played a pivotal role in the Watcher's market positioning. I spearheaded branding initiatives, including promotional design, strategic messaging, and the creation of the Kickstarter campaign, which garnered over $75,000 in pre-launch sales.

Finally, I engineered a proof-of-concept MVP that integrated Sony's Edge AI camera with Seeed's reComputer R1000, leveraging TensorFlow Lite for real-time video inference and Flask for streamlined workflows, demonstrating the potential of scalable edge AI applications.

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