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Member of Technical Staff (Machine Learning Engineer, Search)

Join Perplexity AI as a Machine Learning Engineer to advance search technologies focused on retrieval and ranking.

Location
Belgrade
Compensation
Not disclosed
Level
senior
Type
full time

Posted by employer 1 year ago

First seen on Joblaze 1 week ago

Last verified on the company career page 1 day ago

Apply at Perplexity AI → Save job Scanned from perplexity.ai

Requirements

Experience
5+ years

Not disclosed in this posting: compensation, work arrangement, visa sponsorship.

Joblaze summary

In this role, the Machine Learning Engineer focuses on enhancing search quality through the development and deployment of advanced retrieval and ranking models. Key skills include expertise in search systems, large-scale model training, and the ability to optimize RAG pipelines. This position is ideal for a seasoned professional with at least five years of experience in search or recommendation systems, who thrives in a collaborative environment. Perplexity AI emphasizes innovation in search technology, making it a dynamic place for those passionate about pushing boundaries.

Joblaze insights

Quick facts

How much experience is required?
At least 5 years of relevant experience for this Member of Technical Staff (Machine Learning Engineer, Search) role.
What's the tech stack?
Joblaze extracted these technologies from the posting: LLMs, Machine Learning, Ranking, Retrieval, Search.
What seniority level is this role?
Perplexity AI targets senior candidates for this position.
Is this full-time or contract?
Full-time for this Member of Technical Staff (Machine Learning Engineer, Search) role at Perplexity AI.

From the original posting

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