machine learning engineer in search ranking

Perplexity develops search software and focuses on improving search quality through ranking models, data, evaluation, and production infrastructure.

• Improve search quality across the middle and later stages of ranking through models, data, evaluation, infrastructure, and other available levers;
• Own ranking-quality problems end to end by defining evaluations, identifying bottlenecks, building solutions, and shipping 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;
• Make trade-offs across quality, latency, reliability, cost, and engineering complexity;
• Collaborate across Data, AI, Infrastructure, and Product while retaining ownership of the final quality outcome.

• Deep understanding of search or recommender systems and their evaluation;
• Proven ownership of a large-scale production ranking system or a substantial class of quality problems;
• Strong machine-learning and software-engineering skills across data, models, serving, and monitoring;
• Ability to drive ambiguous, cross-team problems without continuous task decomposition;
• Exceptional depth in either modern neural ranking methods or low-latency ranking systems and runtime;
• Minimum 5 years of relevant industry experience.

• No conditions specified