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Perplexity

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

Belgrade, Serbia

The employer does not state the work arrangement.

Locations: Belgrade, Serbia · London, United Kingdom

Apply on the employer's siteOriginal listingJSON

PeopleSeek Talent does not submit applications. The button opens the employer's own page; this listing was verified 15 minutes ago.

PayNot listed
Work arrangementNot stated
Who can applyNot stated by the employer
LevelLead
TypeFull time
OccupationNot classified yet
LanguageNot stated
Posted12 August 2026
Last verified15 minutes ago
StatusActive

About the 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.

Listed via Perplexity · first seen 22 October 2026 · last verified 28 October 2026

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