Quant Research Engineer

Millennium • Full Time • Hong Kong, HK • 9h ago

Please direct all resume submissions to QuantTalentASIA@mlp.com and reference REQ-30162 as the subject.

Job Specification: Quant Research Engineer

Preferred Candidate Profile

Top-tier academic background from a globally top-20 university (e.g., MIT, Harvard, Princeton, Stanford, Caltech)
PhD-level training in Computer Science, Engineering, Physics, Mathematics, or Statistics preferred
Gold medal in a national or international olympiad (IMO, CMO, IOI, NOI, IPhO, CPhO) strongly preferred
Prior experience at a top-tier quantitative trading firm or a leading AI/technology company preferred
Demonstrated passion for applying AI — candidates who have built LLM-powered tools into their own research or engineering workflow stand out

Key Responsibilities

Core Infrastructure Ownership

Design, build, and maintain the firm’s core quant pipelines, data infrastructure, and research and production compute environments.
Ensure the reliability, scalability, and performance of critical systems central to our research and trading activities.
Drive the architectural vision for our next-generation data and compute platform — including how AI-native capabilities (LLM services, agentic workflows, retrieval infrastructure) are embedded into the research stack.

Collaboration & Integration

Partner directly with Quantitative Researchers and other development teams to understand their requirements and integrate new components into the core infrastructure.
Act as a central point of expertise, facilitating the seamless flow of data and computation across teams and systems.
Identify where AI can accelerate the research process — from literature ingestion and data exploration to signal prototyping — and build the tooling that makes it routine.
Establish and enforce rigorous standards for system design, code quality, testing, and deployment.

DevOps & AI-Augmented Operations

Own the deployment, monitoring, and operational health of production and research systems.
Implement robust observability, logging, and alerting frameworks; apply AI-assisted techniques (automated log analysis, anomaly detection, intelligent incident triage) to raise the bar on reliability.
Drive infrastructure-as-code practices and automate operational workflows, leveraging AI coding agents and LLM tooling where they demonstrably improve velocity and quality.

Qualifications & Experience

3–5 years of professional experience in a quantitative development role, focused on building and maintaining quantitative research and production pipelines. Alternatively, significant engineering experience in a fast-paced startup — or strong hands-on AI/LLM engineering experience (building production LLM applications, agentic systems, or AI-powered developer

tooling) — with demonstrated ownership of complex infrastructure will be considered in lieu of direct quant experience.

Proven, end-to-end ownership of a significant piece of trading, research, high-performance, or AI infrastructure.
Deep expertise in modern C++ and Python in a high-performance computing context.
Demonstrable experience with large-scale data infrastructure (e.g., real-time/streaming and historical tick data).
Strong background in cloud computing (AWS, GCP, or Azure) and parallel computing paradigms.

Hard Skills & Technical Knowledge

Broad knowledge of the technology landscape and the judgment to select the right tool for the problem (e.g., KDB+, Apache Spark, Dask, Redis).
Practical experience applying LLMs and agentic workflows to real engineering or research problems — LLM APIs, agent frameworks, retrieval-augmented generation, and structured output pipelines — with sound judgment about where AI adds value and where determinism must be preserved.
Proficiency with different database designs — SQL, NoSQL, and distributed file systems.
Experience with containerization and orchestration technologies (Docker, Kubernetes).
Strong experience with DevOps practices: infrastructure-as-code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, GitLab CI), and system observability — including familiarity with AI-assisted operations tooling.

Soft Skills

Exceptional Logical & Reflective Thinking: Ability to deconstruct complex problems and design elegant, effective solutions.
Proactive Collaboration: A team player who thrives in a collaborative environment and builds strong partnerships.
High Integrity: Takes initiative and ownership of projects, upholding rigorous ethical standards in handling sensitive data and models.
Growth Mindset: Innate curiosity and commitment to continuous improvement — including genuine enthusiasm for the rapidly evolving AI landscape and a track record of adopting new tools ahead of the curve.
Superb Communication: Can articulate complex technical concepts to both technical and non-technical stakeholders.

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