About the role
Advance research and development across retrieval, ranking, matching, and generative models. Leverage and improve SLMs/LLMs train, fine-tune, and align models and productionize them. Evolve the Ads ranking platform toward better usability, reliability, scalability, efficiency, and architectural coherence. Provide technical leadership on projects: set direction, coach a distributed team, and influence cross-org strategy. Follow research trends in AI to guide the group keep solutions state-of-the-art. - Collaborate with research and engineering teams. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research). OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research). OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research). These
requirements include but are not limited to the following specialized security screenings: Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research). OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics, predictive analytics, research). OR equivalent experience. Experience designing and scaling recommendation systems with massive query/item spaces and multi-stage ranking pipelines. Proven ability to influence platform architecture and align cross-team roadmaps. Track record of publications in tier-1 venues such as NeurIPS, ICML, KDD, WWW, ACL, SIGIR. 8+ years of experience in ML with a proven record of shipping large-scale models to production. Hands-on experience with SLM/LLM training, fine-tuning, and post-training. Proficiency with deep learning frameworks (e.g., PyTorch, Hugging Face, TensorFlow) and distributed training on large datasets.
