Internship - Search Machine Learning Engineer
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Get Started FreeAbout interviewing at Perplexity
One of the few AI startups with a fully published interview guide (perplexity.ai/hub/careers/interview-guide): online application (response within two weeks) → recruiter phone screen → a technical screen that for engineers is 'usually a standard technical programming interview' → a quickly-scheduled onsite of 4–5 interviews including a hiring-manager deep dive on past work and experience anecdotes → a final interview with a Perplexity founder or leader → decision within a week of the onsite. Coding leans Python and mixes LeetCode medium–hard with practical search-flavored tasks (ranking/filtering, concurrency, data handling); system design is AI-native (RAG pipelines, retrieval at scale, LLM serving cost/latency). Applicants are judged 'solely on merit and potential impact' and must show 'frontier knowledge and excellence in at least one area'; roles are broad by default with team matching happening during the onsite, every role — managers included — is hands-on, and building AI products isn't expected but fluency in using AI tools is required. In-person 4 days/week near an office; remote is case-by-case.
Read the full Perplexity interview process →Description
Perplexity is looking for a Search Machine Learning Engineer Intern to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. You will work closely with experienced engineers to improve search quality, experiment with new models, and ship features that directly impact how users search and discover information.
Internship program: 12 - 24 weeks, full-time, in-person in the Belgrade office.
Responsibilities:
Contribute to experiments that improve search quality through better models, data usage, and evaluation tools, under the guidance of senior engineers.
Design and implement components of the search platform and model stack, including retrieval, ranking, and classification models.
Train evaluating models (including LLM-based approaches) for retrieval, ranking, and classification tasks.
Support deployment and monitoring of search and ranking models in a scalable and performant way.
Help build and iterate on RAG pipelines for grounding and answer generation.
Collaborate with Data, AI, Infrastructure and Product teams to deliver improvements quickly and learn best practices in production ML.
Qualifications:
Strong foundation in machine learning and statistics, with coursework or projects related to information retrieval, ranking, or recommender systems.
Experience with Python and common ML frameworks (e.g. PyTorch, TensorFlow, JAX) through academic, open source, or personal projects.
Familiarity with evaluating model quality using offline metrics and/or A/B testing is a plus, but not required.
Previous experience (internships, research, or significant projects) working on search, recommendation, or NLP is a plus, but not required.
Self-driven and curious, with a strong sense of ownership, willingness to learn, and comfort working in a fast-paced environment
Experience with Rust will be a plus
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