About this role
Meta is looking for a Research Engineer to join its Fundamental AI Research (FAIR) organization, within Meta Superintelligence Labs. We publish groundbreaking papers and release frameworks/libraries that are widely used in the open-source community. We closely collaborate with other organizations at Meta to bring the latest research findings to production.
The team where this role sits is exploring new approaches to making AI safer, fighting deepfakes and preventing harmful content. Past work in that space includes watermarking models such as TextSeal, AudioSeal, or VideoSeal, and downstream extensions such as data traceability and benchmark leakage detection. The mission is to develop state-of-the-art algorithms that can protect our users across our platforms, with a focus on open research and scientific novelty.
Responsibilities
Perform research to tackle unsolved real-world problems and push the state-of-the-art in AI provenance, including watermarking
Drive software design, implementation and evaluation of research solutions
Work with a globally distributed organization spanning several expertise areas (research, engineering, external partners) to integrate the research into Meta products
Qualifications
Research or applied research experience in one or more of these areas: audio generation, video generation, machine learning, deep learning, or related fields
Experience working with machine learning libraries like Pytorch, Tensorflow, etc
Experience deploying AI solutions in production
Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Experience working and communicating cross functionally in a team environment
Experience with AI provenance technology
Proven track record of achieving significant results as demonstrated by publications at leading conferences such as ACL, EMNLP, ICASSP, INTERSPEECH, ICCV, CVPR, ICLR, ICML, NeurIPS or similar
Experience with large scale AI Frontier model training, implementing algorithms, and evaluating models