Company
Technology
SeniorMachineLearningEngineer,Search&Recommendations
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Machine Learning Engineer, Search & Recommendations. Skills: Machine Learning, Ranking systems, Recommendations, Optimization. Architect scalable ranking systems. Develop scalable ranking systems”
Industry & Context.
What They're Looking For.
Must Have
4+ years industry experience, 2+ years with PhD, Python proficiency, SQL proficiency, Pandas proficiency, TensorFlow experience, PyTorch experience, Gradient boosting experience, XGBoost experience, Ranking systems understanding, Personalization understanding, Recommendation architectures understanding, Online experimentation experience, A/B testing experience, Advanced evaluation methods experience, Multi-task learning architectures experience, Causal inference experience, Uplift modeling experience, Contextual bandits experience, Low-latency ML systems experience, Feature pipelines experience, Caching experience, Retrieval systems experience, Inference optimization experience
Nice to Have
LLMs for feature enrichment, LLMs for embeddings, LLMs for retrieval augmentation
What You'll Do.
Architect scalable ranking systems
Develop scalable ranking systems
Design multi-task learning models
Implement multi-task learning models
Build value-aware models
Build long-horizon models
Maximize incremental impact
Develop production-grade ranking pipelines
Maintain production-grade ranking pipelines
Develop inference systems
Maintain inference systems
Develop re-ranking layers
Maintain re-ranking layers
Develop constraint-aware decisioning
Maintain constraint-aware decisioning
Enhance search experiences
Enhance discovery experiences
Develop personalized autosuggest
Develop retrieval systems
Design online experiments
Execute online experiments
Design A/B testing frameworks
Execute A/B testing frameworks
Design counterfactual evaluation methods
Execute counterfactual evaluation methods
Collaborate cross-functionally
Translate business objectives
How You'll Work.
Team & Collaboration
Cross-functionally with Ads; Cross-functionally with Product; Cross-functionally with Infrastructure; Cross-functionally with Design
Full Job Description
## Accountabilities Architect and develop scalable ranking systems that unify search, recommendations, ads, and merchandising into a single multi-objective framework. Design and implement multi-task learning models (e.g., shared encoders, MMOE/PLE architectures) to jointly optimize relevance, conversion, margin, churn risk, and other business signals. Build and improve value-aware and long-horizon optimization models, including uplift and causal inference approaches to maximize incremental impact and LTV. Develop and maintain production-grade ranking pipelines, including inference systems, re-ranking layers, and constraint-aware decisioning. Enhance search and discovery experiences, including personalized autosuggest and retrieval systems powered by ML and LLM-enhanced features. Design and execute large-scale online experiments, A/B testing frameworks, and counterfactual evaluation methods to measure impact beyond short-term metrics. Collaborate cross-functionally with Ads, Product, Infrastructure, and Design teams to translate business objectives into ranking strategies and measurable outcomes. Mentor and guide other ML engineers on ranking systems, causal modeling, and scalable ML infrastructure. Requirements: 4+ years of industry experience applying machine learning at scale (or 2+ years with a PhD), with proven impact on ranking or recommendation systems. Strong experience with multi-objective optimization in production environments, balancing relevance, revenue, and user experience. Proficiency in Python and strong data skills using SQL, Pandas, and related tools. Hands-on experience with ML frameworks such as TensorFlow or PyTorch and classical ML methods like gradient boosting (e.g., XGBoost). Solid understanding of ranking systems, personalization, and recommendation architectures. Experience with online experimentation, A/B testing, and advanced evaluation methods beyond CTR-based metrics. Familiarity with multi-task learning architectures (MMOE, PLE, share
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