Job Details

ML Developer for the Search Quality Team for AI at Yandex. Responsibilities include improving retrieval quality, Context Engineering, and infrastructure acceleration. Requires experience in applying ML in production and understanding of NLP.

The world is changing rapidly: more and more search scenarios are being covered by AI chatbots, and we are actively participating in this race. Modern AI, such as Alice, not only answers from memory but also actively searches external sources from the internet — and the quality of the provided sources is critically important for a good answer. Our E2E team is responsible for the quality of search for AI. Join us and help invent the search of the future! What tasks await you: • Improving retrieval quality Currently, our AI services operate on top of Yandex's traditional Search with minor improvements. Search is significantly optimized for human consumption with all its peculiarities. You will have to significantly rework the search stack for AI consumption to provide the most relevant and expert sources for each query. Together with our analytics team, you will analyze losses and weaknesses, develop new quality signals, and implement models. • Improving Context Engineering It's not enough to find documents — you need to prepare good context from them for LLM. There are many open challenges here: how to extract only what is important and necessary without breaking the meaning and staying within strict production constraints? You will have to find the answers. • Accelerating infrastructure and saving costs Search is expensive. We spend a lot of hardware, and the RPS from agent systems is higher than from humans. Together with the infrastructure team, you will have to optimize the search stack without losing quality.

We expect you to: • Have high engineering and ML culture, have applied ML in production • Possess broad knowledge and experience in modern NLP • Understand that the main thing in ML is data, be ready to look at it extensively and closely • Be able to critically assess metrics and model results • Want to see the product and the user behind the numbers

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