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

Join Perplexity AI as a Machine Learning Engineer to enhance search quality through innovative ranking solutions.

Location
Belgrade
Compensation
Not disclosed
Level
senior
Type
full time

Posted by employer 1 month ago

First seen on Joblaze 1 week ago

Last verified on the company career page 13 hours ago

Apply at Perplexity AI → Save job Scanned from perplexity.ai

Requirements

Experience
5+ years

Not disclosed in this posting: compensation, work arrangement, visa sponsorship.

Joblaze summary

In this role, the Machine Learning Engineer focuses on enhancing search quality by tackling complex ranking challenges from start to finish. Key skills include expertise in neural ranking methods and production systems, alongside strong software engineering capabilities. This position is ideal for seasoned professionals with at least five years of experience in search or recommender systems, who can navigate ambiguous problems and collaborate effectively across teams. Perplexity AI emphasizes a hands-on approach to improving search infrastructure and model performance.

Joblaze insights

Quick facts

How much experience is required?
At least 5 years of relevant experience for this Member of Technical Staff (Machine Learning Engineer, Ranking Quality - Search) role.
What's the tech stack?
Joblaze extracted these technologies from the posting: LLM, Machine Learning, Neural Networks, Ranking Systems, Search.
What seniority level is this role?
Perplexity AI targets senior candidates for this position.
Is this full-time or contract?
Full-time for this Member of Technical Staff (Machine Learning Engineer, Ranking Quality - Search) role at Perplexity AI.

From the original posting

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.