Perplexity is seeking an experienced Machine Learning Engineer to improve search quality across the middle and later stages of ranking. We are looking for a strong ranking generalist who can own ambiguous problems end to end and brings exceptional depth in either neural ranking or production ranking systems.
Responsibilities
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Relentlessly push search quality forward through models, data, evaluation, infrastructure, or any other leverage available.
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Own ranking-quality problems end to end: define the evaluation, identify the bottleneck, build the solution, and ship it safely.
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Train and evaluate retrieval, ranking, and classification models, including neural and LLM-based approaches where appropriate.
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Build and operate ranking infrastructure, including feature computation, low-latency inference, multi-stage cascades, deployment, and monitoring.
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Make sound trade-offs across quality, latency, reliability, cost, and engineering complexity.
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Collaborate across Data, AI, Infrastructure, and Product while retaining ownership of the final quality outcome.
Qualifications
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Deep understanding of search or recommender systems and their evaluation.
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Proven ownership of a large-scale production ranking system or a substantial class of quality problems.
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Strong machine-learning and software-engineering skills across data, models, serving, and monitoring.
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Ability to drive ambiguous, cross-team problems without continuous task decomposition.
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Exceptional depth in either modern neural ranking methods or low-latency ranking systems and runtime.
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Minimum 5 years of relevant industry experience.
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