Cher Feng is a dedicated Technical Sourcing Recruiter at Amazon, specializing in the rapidly evolving field of machine learning for Alexa. With a profound passion for new technology, Cher plays a pivotal role in identifying and attracting top-tier talent to support Amazon's ambitious projects across...
Cher Feng is a dedicated Technical Sourcing Recruiter at Amazon, specializing in the rapidly evolving field of machine learning for Alexa. With a profound passion for new technology, Cher plays a pivotal role in identifying and attracting top-tier talent to support Amazon's ambitious projects across various domains, including Ads, Prime Video, and AWS. Her expertise lies in recruiting for cutting-edge areas such as generative AI, large language models (LLM), recommendation systems, and reinforcement learning, which are crucial for enhancing user experiences and driving innovation within the Alexa ecosystem.
Cher's approach to technical recruiting is characterized by her strong understanding of the complexities involved in sourcing candidates with specialized skills. She excels in onboarding processes, ensuring that new hires are seamlessly integrated into virtual teams and equipped to contribute effectively from day one. Her background in account management and vendor management further enhances her ability to collaborate with diverse stakeholders, ensuring that recruitment strategies align with organizational goals.
Beyond her professional endeavors, Cher is committed to helping individuals find the right career fit, leveraging her skills in career management to guide candidates through the recruitment journey. When she’s not immersed in the world of technology and talent acquisition, Cher enjoys quality time with her family, indulging in cooking, camping, and running. She also shares a love for storytelling, often unwinding by watching "Game of Thrones" with her loyal dog by her side. Cher Feng embodies the intersection of technology and human connection, making her an invaluable asset to Amazon’s recruitment efforts in the dynamic landscape of machine learning.
