Perplexity
Member of Technical Staff (Machine Learning Engineer, Ranking Quality - Search)
Belgrade, US
Role snapshot
Perplexity is hiring a Member of Technical Staff (Machine Learning Engineer, Ranking Quality - Search) on a full-time basis. This is a role based in Belgrade, US. The listing highlights Search.
- Company
- Perplexity
- Location
- Belgrade, US
- Job type
- Full-time
- Posted
- Oct 2, 2026
Who this role suits
The listing does not state a required experience level here. Read the requirements in the description below and apply if they match your background.
How to prepare your application
- Check your résumé against the listed skills (Search) and give each one a concrete example: what you built or achieved, not just that you used it.
- Match your résumé to the requirements in the description, using the same wording where it is honest to do so.
- Prepare a short example for each requirement you meet, and be ready to explain any you only partly meet.
- Read what Perplexity publishes about its product or team, so you can say clearly why this role and this company.
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About the role
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
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.