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Research Scientist, Machine Learning (PhD) or Research Scientist, Systems and Infrastructure (PhD)

Min Experience

0 years

Location

remote

JobType

full-time

About the job

Info This job is sourced from a job board

About the role

Accelerate Eng Talent is an innovative program that provides a fast-track for incoming Research Scientists to become AI specialists. Candidates applying to Research Scientist Machine Learning or Systems and Infrastructure roles will be placed on critical generative AI or AI Infrastructure teams, where they will contribute to pivotal projects while honing specialized AI skills. Are you a graduating PhD candidate interested in launching your career in AI? At Meta, we're pushing the boundaries of AI through research, infrastructure and product innovation. The Accelerate Eng Talent program provides a path to specialize in niche AI domains. Participants will work on challenging engineering problems, use state-of-the-art tools to apply AI at scale, and build a network of peers and mentors. The Accelerate Eng Talent program assesses AI skills, experience and interest at the time of interview. We then pair PhD candidates pursuing Research Scientist roles at Meta with high-priority AI-focused teams based on skills and interest alignment. Participants will spend 12 months in the program, honing their skills and developing deep subject matter expertise and AI specialization through complex projects on our roadmaps. Upon successful completion of the program, participants will be recognized as an AI specialist at Meta. In the AI Infrastructure space, we're looking for domain skills like computer vision, distributed systems, compilers, optimization, and large language model fine-tuning. In the generative AI space, we're looking for domain skills like multimodal generation and understanding, pre/post training language modeling and trust/safety.

About the company

Meta's mission is to build the future of human connection and the technology that makes it possible.

Skills

computer vision
distributed systems
compilers
optimization
large language model fine-tuning
multimodal generation and understanding
pre/post training language modeling
trust/safety