Skills

Full description

Perplexity is seeking an experienced Machine Learning Engineer to improve search quality across the middle and later stages of ranking
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

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
Ability to drive ambiguous, cross-team problems without continuous task decomposition
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
Exceptional depth in either modern neural ranking methods or low-latency ranking systems and runtime
Minimum 5 years of relevant industry experience
Strong machine-learning and software-engineering skills across data, models, serving, and monitoring

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

Compensation

Not disclosed

Location

London, United Kingdom; Belgrade, Serbia

Work setup

Not specified

Seniority

Not specified

Employment

Not specified

Experience

5+ years

Perplexity is seeking an experienced Machine Learning Engineer to improve search quality across the middle and later stages of ranking
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

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
Ability to drive ambiguous, cross-team problems without continuous task decomposition
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
Exceptional depth in either modern neural ranking methods or low-latency ranking systems and runtime
Minimum 5 years of relevant industry experience
Strong machine-learning and software-engineering skills across data, models, serving, and monitoring