Research Scientist - Gen AI & User Representation Learning

About Us: We are looking for an exceptional Research Scientist to develop next-generation AI technologies, focusing on user representation learning, semantic understanding, and generative AI applications. You will conduct applied research that advances representation learning, multimodal understanding, and transformer-based modeling while working closely with engineering teams to translate research into production systems. The ideal candidate combines strong scientific thinking with practical engineering skills and enjoys solving challenging problems using large-scale real-world data. Responsibilities: Conduct Applied AI Research Research and develop novel machine learning algorithms for user representation learning, semantic embeddings, and foundation-model applications. Design, prototype, evaluate, and deploy transformer-based generative AI solutions from research through deployment. Develop scalable representation learning techniques using transformers, contrastive learning, self-supervised learning, and retrieval-based architectures. Investigate multimodal learning approaches that jointly model structured, behavioral, textual, and other heterogeneous data. Build Large-Scale AI Systems Train and evaluate models using large-scale behavioral, transactional, social, temporal, and content datasets. Design embedding models, retrieval systems, vector databases, and semantic search pipelines. Collaborate with platform and infrastructure engineers to deploy production-quality AI models. Design rigorous offline and online evaluation methodologies and establish reproducible benchmarking pipelines. Collaborate Across Teams Work closely with product, engineering, and domain experts to identify impactful research opportunities. Translate ambiguous business problems into measurable machine learning objectives. Communicate research findings clearly to both technical and non-technical audiences. Contribute to the long-term AI research roadmap and technical strategy. Requirements: Education PhD (completed or near completion) in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative discipline. Equivalent industrial research experience will also be considered. Technical Expertise Strong background in one or more of the following: Deep Learning Representation Learning Transformer architectures Generative AI Models Contrastive Learning Self-supervised Learning Embedding Models Retrieval-Augmented Generation (RAG) Vector Search Semantic Search Information Retrieval Experience with: Python PyTorch (preferred) or JAX Large-scale distributed data processing Model experimentation and evaluation End-to-end machine learning system development GPU Computing NVIDIA GPU architecture and CUDA programming fundamentals Multi-GPU and distributed training using PyTorch Distributed Mixed precision training (FP16/BF16/FP8) Profiling and optimizing GPU utilization, communication overhead, and training throughput Research Mindset Candidates should demonstrate: Strong scientific rigor Ability to establish meaningful baselines before pursuing more complex models Well-designed experiments and reproducible evaluations Data-driven decision making Intellectual curiosity and independent problem solving What We're Looking For: Combine research excellence with strong engineering execution. Enjoy working with ambiguous, real-world business problems. Can independently drive projects from idea to production. Thrive in highly collaborative, cross-functional environments. Have excellent written and verbal communication skills. Are passionate about building practical generative AI systems that create measurable business impact. 5 years of industrial or applied research experience preferred (including internships).