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.

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