AI Engineer

AI Engineer

Location: Hong Kong / Shanghai

About Millennium

Millennium is a global, diversified alternative investment firm founded in 1989, focused on delivering high-quality returns for investors. The firm manages more than $92 billion in AUM and operates a global platform with 6,800+ employees, 140+ locations, and 340+ investment teams worldwide. Our business is built on evolution and innovation. We combine entrepreneurial talent, disciplined risk management, and significant investment in technology to support the platform.

About the Team

You will work with one of Millennium’s investment teams, which primarily focuses on Fundamental Equity. The team is building AI-enabled research capabilities that bring together proprietary knowledge, public information, documents, data, APIs, and analytical tools to improve company and industry research, market monitoring, and investment decision-making.

About the Role

We are seeking an AI Engineer to work directly with investment professionals and own practical AI projects from problem definition and prototyping through deployment, evaluation, and ongoing improvement. The successful candidate will combine strong engineering ability with curiosity about fast-moving AI technologies and sound judgment about how to turn emerging capabilities into reliable research products.

Responsibilities

• Design, build, and maintain AI agents and automated workflows for company research, industry analysis, earnings, market monitoring, and event-driven work.
• Develop tools that connect internal knowledge, public information, documents, data, APIs, and analytical systems.
• Build reliable multi-source data and document workflows for research use cases, including ingestion, transformation, quality checks, and structured outputs.
• Evaluate new models, developer tools, and open-source projects, and rapidly test promising approaches such as LLM Wiki-style knowledge systems and loop-based engineering workflows.
• Translate emerging AI capabilities into useful research products rather than standalone demos.
• Partner with investment users to define requirements, communicate trade-offs, and iterate based on evidence and feedback.
• Establish evaluation frameworks covering accuracy, source quality, usefulness, reliability, latency, and cost.
• Improve tooling, documentation, and reusable components to reduce manual work and support durable adoption across the team.

What You Bring

• Bachelor’s, master’s, or PhD degree in Computer Science, Artificial Intelligence, Software Engineering, or a closely related technical field.
• 1–3 years of professional, full-time experience in AI engineering, machine-learning engineering, software engineering, or a closely related role.
• Strong software-engineering skills in at least one modern programming language; familiarity with APIs, databases, data pipelines, and cloud-based development environments.
• Hands-on experience building with LLMs, AI agents, tool-using systems, automation frameworks, or open-source AI projects.
• Demonstrated ability to turn ambiguous user needs into working products and deliver projects from initial prototype through deployment and improvement.
• Ability to explore unfamiliar technologies quickly while maintaining sound standards for testing, reliability, and documentation.
• Strong interest in new AI models, GitHub projects, research papers, and developer communities.
• Clear communication skills and professional fluency in English and Mandarin.
• Interest in investing, companies, and industry research. Prior investment or financial experience is a plus, not a requirement.

Preferred Qualifications

• Experience at a leading AI company, foundation-model company, AI developer-platform business, or agent-focused company or startup.
• Experience building and operating agentic systems, multi-step workflows, or AI products used by real users.
• Familiarity with document processing, browser automation, retrieval and knowledge systems, data visualization, model evaluation, or real-time data systems.
• Evidence of technical depth through meaningful production projects, research, or open-source contributions.

How We Work

• Ownership: Define problems clearly, deliver end-to-end, and follow through on outcomes.
• Curiosity: Track emerging technologies and distinguish useful advances from hype.
• Practicality: Start with the simplest viable approach and use advanced methods where they add measurable value.
• Rigour: Care about accuracy, source quality, reproducibility, reliability, and honest evaluation.
• Collaboration: Work closely with investment and technology colleagues, seek feedback, and communicate complex ideas clearly.