Senior Machine Learning Engineer, Search & Recommendations Ranking

Architect and build multi-task, multi-objective ranking systems that unify search, recommendations, ads, and merchandising. Design long-horizon value models, causal inference systems, and low-latency inference layers while mentoring ML engineers.

About the job

What We're Building

Foundational Ranking Backbone Models: Multi-task/multi-objective models (shared encoders + task heads) that jointly learn relevance, conversion, margin contribution, churn risk, and ad quality
Value-Aware Optimization: Uplift and long-horizon value models that steer decisions toward incrementality and LTV, with calibrated constraints on quality, diversity, fairness, and spend pacing
LLM-Enhanced Retrieval & Features: Using LLMs to enrich query and item semantics for long-tail recall, generate features for cold-starts, and feed the ranker with reasoning-rich context

About the Job

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
Develop production-grade Multi-Task Learning (e.g., shared encoders, MMOE/PLE task heads) to jointly learn relevance, propensity, margin, and churn risk
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
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

Minimum Qualifications

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

Preferred Qualifications

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)

Skills

Python, SQL, pandas, Xgboost, TensorFlow, PyTorch, Multi-Task Learning, Mmoe, Ple, Causal Inference, Contextual Bandits, Feature Stores, A/B Testing, LLMs