Condi
•Design, develop and maintain applications based on artificial intelligence technologies such as natural language processing, computer vision, machine learning, etc. •Successfully integrate AI models into existing software systems and applications to achieve practical applications. This may include developing APIs, microservices, and cloud deployments. •Deploying AI models in cloud environments has become a common practice, using cloud service providers (such as Azure, AWS, Google Cloud, etc.) to host and deploy models to ensure high availability and performance.
- Experience
- 8+ yrs
- Based in
- US
- Speaks
- English & Chinese
About
As an AI scientist at Meta, I have a diverse technical background covering artificial intelligence, machine learning, reinforcement learning, ethics, and other fields.
My goal is to find solutions to the development of artificial intelligence to prevent unforeseen risks, rather than to promote human well-being and the progress of society. I have a comprehensive technical background with expertise in several key areas, including artificial intelligence, machine learning, reinforcement learning, and ethics. This knowledge across disciplines allows me to view super-alliances from both a technical and ethical perspective, striving to create artificial intelligence systems that serve humanity’s best interests. My main goal is to contribute to the development of artificial intelligence systems that are not just technologically advanced, but ethically sound. My goal is to drive the adoption of best practices in AI ethics and design, ensuring that the advances we make in AI technology have a positive impact on society and individuals alike.
Experience
- 2018-10-18 – 1970
US
Work
Let's work together
Condi · •Design, develop and maintain applications based on artificial intelligence technologies such as natural language processing, computer vision, machine learning, etc. •Successfully integrate AI models into existing software systems and applications to achieve practical applications. This may include developing APIs, microservices, and cloud deployments. •Deploying AI models in cloud environments has become a common practice, using cloud service providers (such as Azure, AWS, Google Cloud, etc.) to host and deploy models to ensure high availability and performance.
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