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 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.