# Member of Technical Staff (Machine Learning Engineer, Search) at Perplexity

> Hybrid · Serbia · Staff · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Aperplexity%3A0190699f-010b-44f2-8399-278899fef018 (read 2026-11-14).

## Facts (observed)

- Company: [Perplexity](https://alderhq.app/companies/perplexity)
- Location text: Belgrade; London; Berlin
- Where it can be done from (inferred): Hybrid · Serbia (evidence: "Location: Belgrade; London; Berlin")
- Posted: 2025-03-25 (599 days ago)
- First seen by alder: 2026-11-03 · last verified: 2026-11-12
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/perplexity/0190699f-010b-44f2-8399-278899fef018
- Skills listed (inferred): LLMs

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (London, n 40): p25 £88k, median £120k, p75 £206k

## Description (the employer's text)

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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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aperplexity%3A0190699f-010b-44f2-8399-278899fef018 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.