Quant Research Engineer

About the role

Quant Research Engineer

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