AI Engineer

Summary

Join Pixalate as an AI Research Engineer and bridge the gap between fundamental AI research and production systems that protect the digital ecosystem. Work with the Research team to lead research in emerging AI paradigms, including autonomous agent systems, test-time compute optimization, and multimodal understanding, all applied to real-world challenges in digital safety and fraud detection. You will design and implement multi-agent architectures, develop sophisticated agent coordination systems, create tool-integrated AI agents, and research novel approaches to agent safety and alignment. Responsibilities also include implementing state-of-the-art reasoning systems, optimizing inference-time compute allocation, developing chain-of-thought mechanisms, and building advanced multimodal models for analyzing various data types. You will build advanced multimodal models, develop sophisticated RAG architectures, implement advanced chunking strategies, and research cross-modal learning for fraud pattern detection. This role requires a PhD in a related field and a strong research background with publications in peer-reviewed venues. The ideal candidate will possess expert proficiency in Python and deep learning frameworks, along with advanced experience in various AI frameworks and research tools.

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
• Agentic AI, autonomous systems, or multi-agent coordination
• Multimodal learning or computer vision
• 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
• Agent development and orchestration
• RAG systems and vector databases
• Distributed training frameworks and GPU optimization
• Strong understanding of: Transformer architectures and attention mechanisms
• Reinforcement learning and reward modeling
• Neural architecture search and AutoML
• 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

Responsibilities

• 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: Design 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

Preferred Qualifications

• Experience with fraud detection, cybersecurity, or trust & safety applications
• Contributions to open-source AI projects
• Industry research experience at leading AI labs (DeepMind, OpenAI, FAIR, etc.)
• Experience translating research into production systems
• Experience with: Mixture of Experts (MoE) architectures
• Constitutional AI and alignment techniques
• Efficient inference optimization (quantization, distillation)
• Real-time streaming ML systems

Benefits

• Monthly internet reimbursement
• Casual, remote work environment
• Hybrid, flexible hours
• Opportunity for advancement
• Fun annual team events
• Being part of a high-performing team that wants to win and have fun doing it
• Extremely competitive compensation