Job Description:
• 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:
• 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.
• 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.
• 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.
Benefits: