Чем предстоит заниматься
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