Research Scientist, Gen AI & User Representation Learning

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.
• Develop 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