Quant Research Engineer

Please direct all resume submissions to [email protected] 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.