Quant Research Engineer

Quant Research Engineer*Please direct all resume submissions to****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.