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About the Role

We are seeking a Quant Research Engineer to design, build, and maintain the firm’s core quant pipelines, data infrastructure, and research and production compute environments. This role involves driving the architectural vision for our next-generation data and compute platform, embedding AI-native capabilities, and partnering with researchers to integrate new components. You will also own the deployment, monitoring, and operational health of production and research systems, applying AI-assisted techniques to enhance reliability.

Responsibilities

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 are embedded into the research stack.
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 and build the tooling that makes it routine.
Establish and enforce rigorous standards for system design, code quality, testing, and deployment.
Own the deployment, monitoring, and operational health of production and research systems.
Implement robust observability, logging, and alerting frameworks; apply AI-assisted techniques to raise the bar on reliability.
Drive infrastructure-as-code practices and automate operational workflows, leveraging AI coding agents and LLM tooling.

Requirements

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 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.
Strong background in cloud computing (AWS, GCP, or Azure) and parallel computing paradigms.
Broad knowledge of the technology landscape and the judgment to select the right tool for the problem.
Practical experience applying LLMs and agentic workflows to real engineering or research problems.
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, CI/CD pipelines, and system observability.
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.
Superb Communication: Can articulate complex technical concepts to both technical and non-technical stakeholders.

Skills

Modern C++
Python
High-performance computing
Large-scale data infrastructure
Cloud computing (AWS, GCP, or Azure)
Parallel computing paradigms
KDB+
Apache Spark
Dask
Redis
LLMs
Agentic workflows
LLM APIs
Agent frameworks
Retrieval-augmented generation
Structured output pipelines
SQL
NoSQL
Distributed file systems
Docker
Kubernetes
Infrastructure-as-code (Terraform, CloudFormation)
CI/CD pipelines (GitHub Actions, GitLab CI)
System observability
AI-assisted operations tooling

Experience Level

3-5 years of professional experience

Education Level

PhD-level training in Computer Science, Engineering, Physics, Mathematics, or Statistics preferred
Top-tier academic background from a globally top-20 university

About the Company

Preferred Candidate Profile includes a top-tier academic background from a globally top-20 university (e.g., MIT, Harvard, Princeton, Stanford, Caltech).
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.