Member of Technical Staff (Machine Learning Engineer, Ranking Quality - Search)
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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 seeking an experienced Machine Learning Engineer to improve search quality across the middle and later stages of ranking. We are looking for a strong ranking generalist who can own ambiguous problems end to end and brings exceptional depth in either neural ranking or production ranking systems.
Responsibilities
Relentlessly push search quality forward through models, data, evaluation, infrastructure, or any other leverage available.
Own ranking-quality problems end to end: define the evaluation, identify the bottleneck, build the solution, and ship it safely.
Train and evaluate retrieval, ranking, and classification models, including neural and LLM-based approaches where appropriate.
Build and operate ranking infrastructure, including feature computation, low-latency inference, multi-stage cascades, deployment, and monitoring.
Make sound trade-offs across quality, latency, reliability, cost, and engineering complexity.
Collaborate across Data, AI, Infrastructure, and Product while retaining ownership of the final quality outcome.
Qualifications
Deep understanding of search or recommender systems and their evaluation.
Proven ownership of a large-scale production ranking system or a substantial class of quality problems.
Strong machine-learning and software-engineering skills across data, models, serving, and monitoring.
Ability to drive ambiguous, cross-team problems without continuous task decomposition.
Exceptional depth in either modern neural ranking methods or low-latency ranking systems and runtime.
Minimum 5 years of relevant industry experience.
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