AI Research Engineer

About the Role

We are partnering with a leading global hedge fund that is investing heavily in the next generation of AI-driven research and investment technologies.

As part of this strategic initiative, the firm is seeking an AI Research Engineer to join its growing technology and research platform team in Hong Kong or Shanghai .

This role sits at the intersection of artificial intelligence, software engineering, quantitative research, and investing . You will work closely with portfolio managers, investment professionals, researchers, and engineers to develop cutting-edge AI systems that enhance investment research, knowledge discovery, decision-making, and operational efficiency.

What You'll Do

AI Research & Innovation

• Explore and evaluate the latest advancements in machine learning, LLMs, retrieval systems, agentic workflows, foundation models, and emerging AI architectures.
• Prototype and develop novel AI-driven applications for investment research, market intelligence, knowledge management, and decision support.
• Translate open-ended business and research questions into scalable AI solutions.
• Conduct experiments to assess model quality, reliability, factual accuracy, latency, and cost efficiency.

Platform & Product Development

• Design and build production-grade AI platforms, tools, and services used across investment and research teams.
• Develop end-to-end solutions covering data ingestion, retrieval, model serving, evaluation, monitoring, and continuous improvement.
• Build intelligent search, RAG, knowledge discovery, and agent-based systems leveraging structured and unstructured data.
• Collaborate with engineering teams to deploy scalable APIs, workflows, and cloud-native applications.

Partnership with Investment Teams

• Work directly with portfolio managers, analysts, and researchers to identify high-impact opportunities where AI can improve workflows and decision-making.
• Translate business requirements into measurable technical objectives and practical solutions.
• Drive projects from problem definition through deployment and ongoing iteration.

Technical Leadership

• Influence technology selection, system architecture, and engineering best practices.
• Stay at the forefront of AI, data, and software engineering trends, identifying opportunities to adopt emerging technologies.
• Contribute to a culture of experimentation, innovation, and technical excellence.

What We're Looking For

Technical Excellence

• Strong software engineering fundamentals with production experience building complex systems.
• Excellent programming skills in Python and at least one additional language such as C++, Java, Go, Scala, or Rust.
• Experience designing distributed systems, APIs, data platforms, or large-scale software applications.
• Strong understanding of data structures, algorithms, and system architecture.

AI & Machine Learning

• Hands-on experience building and deploying machine learning or AI-driven products.
• Practical experience with modern LLM ecosystems, including Retrieval-Augmented Generation (RAG), embeddings, vector search, prompt engineering, model evaluation, and agent frameworks.
• Familiarity with deep learning frameworks such as PyTorch, TensorFlow, or equivalent.
• Ability to critically evaluate new models, research papers, and emerging AI technologies.

Product & Business Mindset

• Strong analytical thinking and problem-solving abilities.
• Comfortable working in ambiguous, fast-moving environments with evolving requirements.
• Ability to balance long-term vision with pragmatic delivery.
• Excellent communication skills and ability to collaborate effectively with technical and non-technical stakeholders.

Preferred Qualifications

• Advanced degree (Master's or PhD) in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Physics, Engineering, or a related quantitative discipline.
• Experience working in AI research organizations, technology companies, quantitative funds, or data-driven environments.
• Experience building agentic systems, autonomous workflows, knowledge graphs, or AI-powered productivity platforms.
• Interest in financial markets, investing, quantitative research, or decision-intelligence systems.
• Track record of publications, patents, open-source contributions, or significant production systems.