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Member of Technical Staff (Machine Learning Engineer, Search)

Perplexity · Belgrade

full-timeStaff+

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About the Role

Perplexity is seeking an experienced Machine Learning Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking.

Responsibilities

  • Relentlessly push search quality forward—through models, data, tools, or any other leverage available

  • Architect and build core components of our search platform and model stack

  • Train and evaluate retrieval, ranking and classification models, including LLMs

  • Deploy models - from boosting to LLMs - in a scalable and performant way

  • Build and optimize RAG pipelines for grounding and answer generation

  • Collaborate with Data, AI, Infrastructure and Product teams to ensure fast and high quality delivery

Qualifications

  • Deep understanding of search and retrieval systems, including quality evaluation principles and metrics

  • Proven track record with large-scale search or recommender systems

  • Self-driven, with a strong sense of ownership and execution

  • Minimum of 5 years of working on search or recsys-related projects

In information theory, perplexity is a measure of uncertainty for a discrete probability distribution. The perplexity of a fair coin toss is 2, and that of a fair die roll is 6; and generally, for a probability distribution with exactly N outcomes each having a probability of exactly 1 / N, the perplexity is simply N. But perplexity can also be applied to unfair dice, and to other non-uniform probability distributions. It can be defined as the exponentiation of the information entropy. The larger the perplexity, the less likely it is that an observer can guess the value which will be drawn from the distribution.

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