Senior Machine Learning Engineer II, Ads Response Prediction

Minimum Qualifications

• PhD/Master in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field.
• 6+ years of combined academic and industry experience (including PhD research) applying ML to ranking, recommendation, or prediction problems at scale.
• Deep understanding of CTR/conversion prediction modeling, including familiarity with architectures such as Deep & Wide, DeepFM, DCN, and multi-task learning formulations.
• Strong foundation in causal inference, counterfactual reasoning, and training data bias mitigation. Ability to reason about selection bias, position bias, and propensity-based correction methods.
• Proficiency in Python and deep learning frameworks (PyTorch, Tensorflow, JAX). Fluency in data manipulation tools (SQL, Spark, Pandas).
• Track record of formulating ambiguous problems into well-scoped ML research directions and delivering results through rigorous experimentation.
• Strong written and verbal communication skills. Ability to explain complex modeling decisions to cross-functional stakeholders including product managers and data scientists.

Preferred Qualifications

• Experience in ads ranking or auction-based systems (pCTR, bid optimization, ROAS feedback loops, marketplace dynamics).
• Hands-on experience with autoregressive sequence models for user behavior prediction, generative retrieval, or transformer-based ranking architectures.
• Familiarity with learned representations such as Semantic IDs, product embeddings, or other approaches to reducing feature cardinality and cold-start challenges.
• Experience with transfer learning or domain adaptation techniques (e.g., LoRA, adapter-based fine-tuning) applied to recommendation or ranking models.
• Publication record in top-tier venues (KDD, WWW, RecSys, NeurIPS, ICML, SIGIR, or similar).
• Experience mentoring junior engineers or shaping technical direction for a modeling team.
• Familiarity with LLM-driven approaches to recommendation, including prompt-based personalization and AI-assisted model development (AutoML).

#LI-Remote

Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here.

Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here.…