Member of Technical Staff (Machine Learning Engineer, Search)
Perplexity · Hybrid
MeritLog read this listing from Perplexity's Ashby job board and last checked it on September 12, 2026.
Source: the employer's Ashby job board. Open the original listing for current details.
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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