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Member of Technical Staff (Machine Learning Engineer, Search)

Perplexity · Hybrid

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MeritLog keeps this source-backed description for reference. Availability is not verified, and there is no application link here.

Last seen by MeritLog September 12, 2026Source: AshbySource version: ashby-public-job-posting-v1

Source: the employer's Ashby job board. Open the original listing for current details. Availability is not verified for this retained page.

Job details

Work model
Hybrid
Salary
Not listed by source
Location
Belgrade

What the role asks for

What you'd do

  • 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

What they're asking for

  • Deep understanding of search and retrieval systems, including quality evaluation principles and metricsSkill
  • Proven track record with large-scale search or recommender systemsSkill
  • Self-driven, with a strong sense of ownership and executionSkill
  • Minimum of 5 years of working on search or recsys-related projectsExperience

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job description

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

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