Member of Technical Staff (Machine Learning Engineer, Ranking Quality - Search) at Perplexity

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.