Senior Applied Scientist

• Design and implement advanced LLM-based architectures and agentic systems for real-world product scenarios
• Translate research breakthroughs into production-ready algorithms, contributing to core capabilities such as reasoning, planning, long-term memory, code-gen based design
• Monitor and improve model performance post-deployment through data-driven iteration and error analysis
• Collaborate across teams to deliver robust, scalable models aligned with product objectives and user value
• Contribute to the organization’s scientific direction by identifying research opportunities that drive long-term differentiation

• M.S. or Ph.D. in Computer Science, Machine Learning, or a related field, or equivalent practical experience
• 5+ years of experience in applied machine learning, with a focus on LLMs, agent systems, or reinforcement learning
• Strong hands-on experience with prompt engineering, context engineering, retrieval-augmented generation (RAG), tool use, planning agents, and long-context modeling, etc.
• Familiarity with model training pipelines using PyTorch, TensorFlow, JAX, or similar frameworks, evaluation strategies, and model deployment best practices
• Strong coding and debugging skills, and comfort working in cross-functional, agile environments

Experience on office document generation and related applications is a plus