Ai Engineer

**AI Engineer**:** Employment Type**:Full-Time
**Location**: Remote, Singapore
**Level**: Mid to Senior (PhD Required)
**Transform AI Research into Real-World Impact**: Pixalate is an online trust and safety platform that protects businesses, consumers and children from deceptive, fraudulent and non-compliant mobile, CTV apps and websites. We're seeking a PhD-level AI Engineer to lead cutting-edge research in agentic AI systems, multimodal analysis, and advanced reasoning architectures that will directly impact millions of users worldwide.
Our software and data have been used to unearth multiple high profile criminal and illegal surveillance cases including:

- 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

Our team of lawyers, data scientists, engineers, economists and researchers span globally with presence in California, New York, Washington DC, London and Singapore.
**About the Role**: As an AI Research Engineer at Pixalate, you'll bridge the gap between fundamental AI research and production systems that protect the digital ecosystem. Working with our Research team (AFAC) that has uncovered more than $100M in ad fraud and national security threats, you'll have the autonomy to pursue groundbreaking research while seeing your innovations deployed at scale within months, not years.
You'll 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.
**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**:

- 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.
- Gene