Наши требования
A strong track record building and operating large-scale ML systems in production, ideally recommendation, ranking, or personalization systems
Experience building modern recommendation systems, e.g., learned embeddings, semantic IDs, sequence models over long user histories, and conversational recommendation systems
Deep experience with the full ML engineering lifecycle: training, deployment, monitoring, data consistency, experimentation, and governance
Strong software engineering fundamentals and fluency in Python and/or Scala, or equivalent languages
Real experience with the operational side of ML: on-call, incident response, debugging systems under load
A track record of technical leadership, whether that's an official title or just being the person a team leans on for the hard calls
Comfort working with ambiguity and translating loose business goals into concrete technical priorities
Good communication skills. You'll need to explain technical tradeoffs to both engineers and non-technical stakeholders
Experience with distributed systems, Spark, or similar large-scale data processing tools is a plus