About this role
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
• 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.
Qualifications
• 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.
Description from Perplexity's public careers feed, reproduced so you can read the role here. Apply on the company's own site; RealAnalystJobs never submits anything for you.
