Member of Technical Staff (Machine Learning Engineer, Search)
Skills & Technologies
Requirements
- Experience
- 5+ years
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 is committed to innovation in search technology, making this a pivotal role in their growth.
Joblaze insights
- Listed about a month ago on Joblaze — tracked directly from Perplexity's career page.
- Perplexity has 59 other open roles including Customer Success & Enterprise Support Lead, APAC , Sales Systems Engineer, Enterprise Operations, Scaled Enterprise Customer Success Manager.
- 270 active Machine Learning roles on Joblaze right now.
- 57 active LLMs roles on Joblaze right now.
- 35 active Search roles on Joblaze right now.
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, Ranking, Machine Learning, Search, Retrieval.
- What seniority level is this role?
- Perplexity 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.
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