We are Creative AI team under Monetization Technology. Our team focuses on developing cutting-edge Generative AI techs across all modalities, including text, image, videos, landing pages, etc., and creates industry-leading technical solutions to improve creative efficiency for advertisers, agencies and creators. We are committed to automated creative workflows by leveraging Generative AI technologies, to increase overall revenue for advertisers, agencies and creators.
We aim to drive and lead generative AI in the ads tech and creative industry, powering products and driving values for our clients, creators, and the whole ecosystem. We are looking for infrastructure engineers who are excited to grow their business understanding, build highly scalable and reliable software/infrastructure, partner across functions with global teams, and make big impacts. If you are someone who welcomes challenges, we are eager to have you on the team!
We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.
Responsibilities:
- Lead research in advancing technology in large language model or multimodality generative model architectures;
- Research, implement and evaluate novel machine learning algorithms on generative models;
- Collaborate with applied machine learning teams and infrastructure teams to ship new generative models into ad production.
Minimum Qualifications
- Individuals who are completing or have recently completed a Master's degree in Computer Science or a related discipline.
- Hands-on experience of developing multimodality foundation models and work or internship experience in an AI research organization is a plus
- Strong publications record in top conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, etc)
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