Member Of Technical Staff
Build and operate machine-learning systems that improve search ranking quality across retrieval and later-stage ranking. The role requires deep search or recommender-systems expertise, production ranking ownership, and at least five years of relevant industry experience.
About the job
Responsibilities
- Push search quality forward through models, data, evaluation, infrastructure, and other available leverage.
- Own ranking-quality problems end to end: define evaluations, identify bottlenecks, build solutions, and ship them safely.
- Train and evaluate retrieval, ranking, and classification models, including neural and LLM-based approaches where appropriate.
- Build and operate ranking infrastructure, including feature computation, low-latency inference, multi-stage cascades, deployment, and monitoring.
- Balance quality, latency, reliability, cost, and engineering complexity.
- Collaborate across Data, AI, Infrastructure, and Product while retaining ownership of final quality outcomes.
Requirements
- Deep understanding of search or recommender systems and their evaluation.
- Proven ownership of a large-scale production ranking system or substantial quality problems.
- Strong machine-learning and software-engineering skills across data, models, serving, and monitoring.
- Ability to drive ambiguous, cross-team problems independently.
- Exceptional depth in modern neural ranking methods or low-latency ranking systems and runtime.
- Minimum 5 years of relevant industry experience.
Skills
Machine Learning, Search Systems, Recommender Systems, Neural Ranking, Ranking Systems, Retrieval Models, Classification Models, LLMs, Feature Computation, Low-Latency Inference, Model Serving, Monitoring, Python, Data Engineering, Evaluation
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