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Perplexity

Member of Technical Staff (Machine Learning Engineer, Ranking Quality - Search)

Perplexity Berlin, Germany

Software Development · 201-500 employees

Aug 12
machine-learning Senior (5-10 yrs) Full-time Germany
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About the role

The engineer will own end-to-end ranking quality problems, including defining evaluation metrics, identifying bottlenecks, and building solutions. They will also design and operate ranking infrastructure, including feature computation, low-latency inference, and model deployment.

What they look for

Machine Learning Search Systems Recommender Systems Neural Ranking LLM Data Modeling Infrastructure Engineering Low-latency Inference Feature Computation Software Engineering Model Evaluation Classification Models Retrieval Systems System Monitoring Deployment

Requirements

Candidates must have at least 5 years of industry experience with a deep understanding of search or recommender systems. Strong proficiency in machine learning, software engineering, and experience with large-scale production ranking systems is required.

Full 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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