Member of Technical Staff (Machine Learning Engineer, Ranking Quality - Search)
At a glance
Senior Machine Learning Engineering role at Perplexity. Belgrade · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Move search quality forward by finding leverage in models, data, evaluation or infrastructure
- Take ranking issues from setting evaluation criteria through finding bottlenecks, building fixes and releasing them safely
- Train and assess retrieval, ranking and classification models, including neural and LLM approaches when useful
- Build and operate ranking infrastructure for feature generation, fast inference, ranking cascades, releases and monitoring
- Choose trade-offs among relevance, speed, reliability, cost and implementation complexity
What you bring
- You understand search or recommendation systems and how to measure their quality
- You have led a ranking system in production at scale, or resolved a substantial ranking-quality challenge
- You can work across machine learning and software engineering, from data and models to serving and monitoring
- You have exceptional expertise in neural ranking methods or in low-latency ranking systems and runtime
Who this fits
This senior role suits a ranking generalist who can own unclear challenges from problem definition through delivery and coordinate across teams without constant task breakdown. The role is based in Belgrade, Serbia.
From the employer
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.