Machine Learning Engineer – Ranking Quality (Search)

About the role

Perplexity is seeking an experienced Machine Learning Engineer to improve search quality across the middle and later stages of ranking. The role requires a strong ranking generalist who can own ambiguous problems end‑to‑end and bring deep expertise in 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 evaluation, identify bottlenecks, build solutions, and ship them safely.
• Train and evaluate retrieval, ranking, and classification models, including neural and LLM‑based approaches where appropriate.
• Build and operate ranking infrastructure, covering 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.
• 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 five years of relevant industry experience.