Senior Staff/Staff Machine Learning Engineer

What You Will Do System Diagnostics: Root cause the bottlenecks of the current search and recommendation systems and quantify the defects across the entire search funnel (query rewriting/ retrieval/ranking). State-of-the-Art Solutions : Propose and architect end-to-end solutions that leverage advanced ML, DL, and Generative AI/LLMs to upgrade traditional search components into AI-native architectures. Core Funnel Optimization with LLMs : Query Understanding : Build next-generation query understanding services (intent detection, query rewrite, semantic expansion, entity extraction) utilizing fine-tuned LLMs. Candidate Retrieval : Design and scale semantic/dense retrieval systems, combining traditional vector search (ANN) with generative retrieval techniques. Ranking & Reranking : Develop and optimize LLM-based listwise/pairwise reranking models, and tackle performance trade-offs (latency, cost, throughput) for real-time serving. Production & Evaluation : Implement solutions, optimize offline/online inference performance (using model distillation, quantization, vLLM/TensorRT), and conduct rigorous A/B tests. Technical Leadership : Be a thought leader, mentor senior engineers, and gain consensus across cross-functional organizations (Infrastructure, Platform, Product) to drive the search AI roadmap. Example Projects Include LLM-Powered Query Understanding : We train and fine-tune in-house LLMs to perform multi-task query tagging, real-time query rewriting/expansion, and precise intent categorization. Generative & Semantic Retrieval : We use deep representation learning, two-tower models, and LLM-generated semantic embeddings combined with vector databases (e.g., Milvus, Vespa) for hyper-accurate candidate retrieval. LLM Reranking & Distillation : We explore listwise and pairwise LLM reranking models to evaluate query-item relevance, while distilling their reasoning capabilities into smaller, sub-millisecond models for real-time serving. Deep Conversion & Personalization : We build Deep Learning-based conversion prediction models ($CVR$) incorporating real-time user state, query intent, and multi-modal product representations. Graph-based Relationships : We leverage graph neural networks (GNNs) and knowledge graphs to ground our LLMs, exploring latent relationships between query sessions and catalog documents. Basic Qualifications 10+ years of professional experience in related fields including Search, Information Retrieval, RecSys, ML, DL, or NLP. Solid software engineering foundation with proficient coding skills in Python, C++, or Java. Distributed Systems Experience : Proven experience handling large data volumes, training large-scale models, and serving high-QPS pipelines in distributed environments (e.g., Spark, Ray, Kubernetes). Pragmatic Problem Solver : Demonstrated ability to take ambiguous business problems, formulate them mathematically, and deliver production-grade ML solutions. Education : Master’s Degree in Computer Science, Data Science, or related engineering fields. Preferred Qualifications LLM & GenAI Expertise : Deep hands-on experience in fine-tuning (SFT, LoRA), aligning (RLHF/DPO), or distilling LLMs specifically for search, recommendation, or NLP tasks (e.g., query expansion, dense retrieval, reranking).