Machine Learning Engineer, Ranking Quality (Search)

About the role

Perplexity is looking for an experienced Machine Learning Engineer to improve search quality across the middle and later stages of ranking. The role suits a ranking generalist who can own ambiguous problems end to end and has exceptional depth in either neural ranking or production ranking systems.

Key responsibilities

• Push search quality forward through models, data, evaluation, infrastructure or any other leverage available.
• Own ranking-quality problems end to end: define the evaluation, identify the bottleneck, build the solution and ship it 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 sound 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.

Required profile

• 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.
• Ability to drive ambiguous, cross-team problems without continuous task decomposition.

Required skills

• Strong machine learning and software engineering skills across data, models, serving and monitoring.
• Exceptional depth in either modern neural ranking methods or low-latency ranking systems and runtime.