Senior Machine Learning Engineer II, Ads Response Prediction

Responsibilities

• Lead research and development of pCTR and conversion prediction models, focusing on calibration, bias reduction, and accuracy across ads surfaces
• Design and implement debiasing techniques including Mixed Negative Sampling (MNS), Inverse Propensity Weighting (IPW), counterfactual risk minimization, and calibration methods (Platt scaling, isotonic regression)
• Contribute to Multi-Domain Multi-Task (MDMT) model architecture with Mixture-of-Experts (MoE), Transformer layers, and LoRA adaptors
• Drive sequence modeling initiatives including TIGER generative retrieval system and Semantic ID representation learning
• Collaborate on Foundation Models using autoregressive user behavior prediction
• Formulate ambiguous modeling problems from first principles and translate business observations into ML research directions
• Publish and present findings internally; contribute to design reviews, paper sharing, and experiment retrospectives

Requirements

• PhD/Master in machine learning, statistics, computer science, information retrieval, or related quantitative field
• 6+ years combined academic and industry experience applying ML to ranking, recommendation, or prediction problems at scale
• Deep understanding of CTR/conversion prediction modeling (Deep & Wide, DeepFM, DCN, multi-task learning)
• Strong foundation in causal inference, counterfactual reasoning, and training data bias mitigation
• Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, JAX); fluency in SQL, Spark, Pandas
• Track record of scoping ML research directions and delivering results through rigorous experimentation
• Strong written and verbal communication skills

Preferred Qualifications

• Experience in ads ranking or auction-based systems
• Hands-on experience with autoregressive sequence models, generative retrieval, or transformer-based ranking architectures
• Familiarity with Semantic IDs, product embeddings, transfer learning, or domain adaptation (LoRA)
• Publication record in top-tier venues (KDD, WWW, RecSys, NeurIPS, ICML, SIGIR)
• Experience mentoring junior engineers
• Familiarity with LLM-driven approaches to recommendation