Tech Stack

Description

You will evolve large-scale recommendation systems and apply AI/LLM technologies to production, focusing on feed, retrieval, and ranking for tens of millions of users. You'll own end-to-end model development from offline training to online inference and A/B testing, while also contributing to 0→1 AI innovation projects.

Requirements

• 3+ years industry experience building ML or recommendation systems in production
• Strong understanding of recommendation fundamentals (ranking, embeddings, experimentation)
• Proficient in Python/Java/Scala and PyTorch or TensorFlow
• Experience with large-scale data and ML systems (e.g., Spark, distributed training)
• Ability to own features end-to-end and deliver measurable business impact

Responsibilities

• Design and iterate on high-throughput, low-latency recommendation systems
• Own systems from offline training to online inference, A/B experimentation, and metric analysis
• Apply LLMs and foundation models for content understanding and user intent modeling
• Prototype and validate AI-driven features then push to production
• Contribute to 0→1 AI-powered projects from exploration to MVP