Senior Machine Learning Engineer II, Search & Recommendations Ranking

Instacart is transforming the grocery industry by enhancing the shopping experience through innovative technology. The Senior Machine Learning Engineer II will architect and develop advanced ranking systems that unify search, personalization, and recommendations to optimize customer engagement and retention.

Responsibilities:

• Architect the ranking backbone that unifies query understanding, personalization, multi-objective ranking, ads, and merchandising into a single adaptive platform
• Design and build a search autosuggest system optimized for personalization and value-based relevance
• Design long-horizon objective functions (e.g., incrementality, LTV, habit formation) and build uplift/causal value models that move beyond short-term engagement
• Develop production-grade Multi-Task Learning (e.g., shared encoders, MMOE/PLE task heads) to jointly learn relevance, propensity, margin, and churn risk—ensuring calibration, constraints, and explainability
• Own the inference layer: goal-aware re-rankers, diversity and quality constraints, safe exploration, and millisecond-class latency optimization
• Advance evaluation practices: online experiments, long-horizon cohort metrics, counterfactual evaluations, and attribution pipelines for tracking incremental GTV and retention
• Partner across ads, infrastructure, product, and design teams to translate business goals into ranking policies and measurable ROI
• Mentor ML engineers to build expertise in ranking, causal inference, and scalable serving systems

Requirements:

• 5+ years applying ML at scale (3+ years in technical leadership), with a proven track record improving ranking or recommendation systems in production
• Demonstrated success in applying multi-objective or constrained optimization to balance relevance, revenue, margin, and user experience; experience with online testing and attribution beyond CTR
• Strong coding (Python) and data fluency (SQL/Pandas), with expertise in classic ML techniques (e.g., XGBoost) and deep learning frameworks (TensorFlow/PyTorch)
• Excellent analytical skills and strong cross-functional communication abilities
• Expertise in multi-task learning architectures (e.g., MMOE/PLE, shared encoders), calibration, counterfactual evaluation, uplift/causal modeling, and/or contextual bandits for exploration
• Experience building low-latency ranking services, including feature stores, caching, vector + lexical retrieval, re-ranking, and A/B testing infrastructure, with expertise in constraint-aware inference
• Hands-on experience with LLMs as feature/recall enhancers (e.g., embeddings, adapter tuning) while maintaining clarity on when the ranker should arbitrate