Senior Machine Learning Engineer II, Ads Response Prediction
Lead research on pCTR and conversion models for Instacart Ads. Tackle bias mitigation, calibration, multi-task learning, and generative retrieval systems. Requires 6+ years ML experience and advanced degree.
About the job
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
Skills
Python, PyTorch, TensorFlow, JAX, SQL, Spark, pandas, Deepfm, Dcn, Mixture-Of-Experts, Transformer, Lora, Ctr Prediction, Causal Inference, Counterfactual Reasoning
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