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We’re creating a world-class academic-industrial hybrid research group to advance recommender systems. Unlike academia, your work won’t live only in papers — it will be deployed into real products, used by millions, and validated at scale. This is your chance to push the science forward and see your ideas transform how the world discovers content.
Job Responsibility:
Drive foundational research to create new recommendation models and paradigms
Leverage rich live user data and large-scale compute to validate models rapidly
Collaborate closely with engineering and product teams to operationalize research
Publish findings and contribute to the broader ML and RecSys community
Requirements:
PhD (or equivalent research experience) in CS, ML, Statistics, or related field
Strong background in deep learning
Proven track record of research excellence (publications, awards, impactful projects)
Proficiency in Python and modern ML frameworks (PyTorch)
Experience with large-scale data and experimentation
Nice to have:
Publications in top venues (NeurIPS, ICML, ICLR, KDD, RecSys, SIGIR, WWW)
Experience with sequential modeling, representation learning, or causal inference
Knowledge of online experimentation and evaluation methodologies
Industry experience deploying ML in production systems
What we offer:
Competitive total compensation package with a pay for performance rewards approach
Equity, and other forms of incentive compensation (as applicable)