AI Engineer

Transform AI Research into Real-World Impact

About the Role

Key Research Areas & Responsibilities

Agentic AI Systems Development

• 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

Advanced Reasoning & Test-Time Compute

• 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

Multimodal AI & Knowledge Systems

• 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

Required Qualifications

Education & Research Background

• 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

Technical Expertise

• 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

Research Skills

• 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

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
We focus on doing things differently and challenge each other to be the best we can be.

• 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

• Gizmodo: An iCloud Feature Is Enabling a $65 Million Scam, New Research Says
• Adweek: A 7-Figure Ad Fraud Scheme Running on Roku Underlines Murkiness of CTV
• Washington Post: Your kids’ apps are spying on them
• Pro Publica: Porn, Piracy, Fraud: What Lurks Inside Google’s Black Box Ad Empire
• ABC7 News: The State of Children's Privacy Online
• NBC News: How many apps are tracking your children

Pixalate is an equal opportunity employer committed to building a diverse team. We particularly encourage applications from underrepresented groups in AI research.