Senior Machine Learning Engineer I & II, Search & Recommendations Ranking

Instacart is transforming the grocery industry by providing essential services for grocery delivery. The role involves architecting a ranking backbone for search and personalization, designing systems optimized for relevance and value, and mentoring ML engineers in advanced ranking techniques.

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