AI Engineer (PhD Required)

What you'll do

• Design and implement multi-agent architectures for autonomous fraud detection and analysis
• Develop sophisticated agent coordination systems using frameworks like LangChain, AutoGen, or custom architectures
• Create tool-integrated AI agents capable of complex reasoning and decision-making
• Research novel approaches to agent safety and alignment in production environments
• Implement state-of-the-art reasoning systems inspired by recent breakthroughs (o1, DeepSeek-R1)
• Optimize inference-time compute allocation for complex analytical tasks
• Develop chain-of-thought and verification mechanisms for high-stakes decision making
• Research novel approaches to scaling reasoning capabilities efficiently
• Build advanced multimodal models for analyzing video, image, text, and behavioral data
• Develop sophisticated RAG (Retrieval-Augmented Generation) architectures including high-performance vector databases and hybrid search systems
• Implement advanced chunking strategies and semantic understanding
• Create context-aware retrieval mechanisms for complex documents
• Research cross-modal learning for fraud pattern detection

Requirements

• PhD in Computer Science, AI, Machine Learning, or related field (or exceptional research track record)
• Published research in peer-reviewed venues demonstrating expertise in Large Language Models and transformer architectures
• Published research demonstrating expertise in Agentic AI, autonomous systems, or multi-agent coordination
• Published research demonstrating expertise in Multimodal learning or computer vision
• Published research demonstrating expertise in Distributed systems and scalable ML
• Expert proficiency in Python and deep learning frameworks (PyTorch preferred, TensorFlow)
• Advanced experience with modern AI frameworks: LangChain, Hugging Face Transformers, Ray
• Experience in agent development and orchestration
• Experience with RAG systems and vector databases
• Experience with distributed training frameworks and GPU optimization
• Strong understanding of transformer architectures and attention mechanisms
• Strong understanding of reinforcement learning and reward modeling
• Strong understanding of neural architecture search and AutoML
• Strong understanding of MLOps and production ML systems
• Track record of novel algorithm development and innovation
• Experience with large-scale experimentation and ablation studies
• Proficiency in research tools: Weights & Biases, MLflow, TensorBoard
• Strong theoretical foundation in optimization, statistics, and linear algebra

Tech stack