Instacart
Grocery
SeniorMachineLearningEngineerII,AdsResponsePrediction
“Senior Machine Learning Engineer II, Ads Response Prediction at Instacart. Skills: ML models, pCTR modeling, Sequence modeling, Retrieval systems. Lead research and development of pCTR and conversion prediction models. Improve calibration, reduce training data biases”
What You'll Achieve.
Improve calibration; Reduce training data biases; Advance model accuracy; Ensure relevant and engaging Ads experience; Optimize for efficient marketplace; Delightful customer shopping experience; Desirable advertiser business outcome; Instacart Ads revenue
Industry & Context.
Formulate ambiguous problems; Scope ambiguous modeling problems; Translate business observations into ML research directions
What They're Looking For.
Must Have
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, Familiarity with architectures such as Deep & Wide, DeepFM, DCN, and multi-task learning formulations, 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, 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, Written and verbal communication skills, Ability to explain complex modeling decisions to cross-functional stakeholders
Nice to Have
Experience in ads ranking or auction-based systems, 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)
What You'll Do.
Lead research and development of pCTR and conversion prediction models
reduce training data biases
Advance model accuracy across ads surfaces
Design and implement debiasing techniques
Address systematic prediction biases
Contribute to next-generation MDMT model architecture
Incorporate innovations like MoE
Drive sequence modeling initiatives
Expand application across ads surfaces
Collaborate on path toward Foundation Models
Formulate and scope ambiguous modeling problems
Translate business observations into ML research directions
Publish and present findings internally
Contribute to technical rigor through design reviews
How You'll Work.
Team & Collaboration
Collaborate with the broader ML community; Explain complex modeling decisions to cross-functional stakeholders
Communication Scope
Written communication skills; Verbal communication skills
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