VP, Principal Quant Engineer

Acadian Asset Management LLC • Boston, MA

6h • $185,000 - $225,000 • Hybrid

About The Position

Acadian Asset Management is a global, systematic investment manager at the forefront of data-driven investing since 1986. Headquartered in Boston, with locations in Singapore, London, and Sydney, we manage over $170 billion on behalf of leading institutions worldwide—including pension funds, endowments, foundations, and sovereign wealth funds. We harness advanced technology, rich datasets, and multidisciplinary expertise to help clients navigate complex markets and uncover insights that may be overlooked by traditional approaches.
What sets Acadian apart is our people. We foster a collaborative, intellectually curious environment where ideas are tested, diverse perspectives are welcomed, and innovation thrives. We’re united by a shared purpose: delivering effective client outcomes and supporting one another in work that’s both challenging and rewarding. We offer a flexible hybrid work environment, strong benefits, and a casual but focused office culture—all designed to support the meaningful, collaborative work that defines Acadian.
Position Overview:
We are looking for a Principal Quant Engineer to work in collaboration with the Research, Portfolio Management and Data teams to design and implement architect solutions for next generation quantitative platform. This platform will be used as the foundation of Acadian’s investment research and production systems that cover alpha, risk, transaction cost and portfolio analysis, etc. The successful candidate will be the key person who drives the design, development and adoption of this platform and associated infrastructure.
Acadian supports a hybrid work environment; employees are on-site in the Boston office 3 days a week.

Requirements

• Bachelor or above degree with proven experience in strong enterprise architecture skills for quantitative research, systematic trading, or data-intensive analytics. Good understanding of quant finance.
• Strong knowledge of modern architectural patterns, including Microservices and APIs, Event-driven and/or streaming architectures, Workflow orchestration and scheduling, Cloud infrastructure and containerization
• 7+ years of experience in software architecture and engineering, with at least 5 years in financial services. Experience in quantitative asset management, hedge funds, or trading working with researchers and portfolio managers is highly preferred.
• 5+ years of hands-on development experience in large scale quantitative systems, utilizing common python scientific computing libraries (numpy, scipy, sk-learn, pandas, polars, pytorch, ray, etc) and common research toolchains.
• Hands-on experience in applying LLM-based systems and agents in production environment. Familiarity with modern AI technologies including RAG, tool-calling/function-calling, MCP, prompt engineering, etc.
• Demonstrated experience improving efficiency, reliability, and observability in complex production systems.
• Creativity, enthusiasm, collegiality and the ability to excel in a self-starting environment.
• Strong work ethics and roll up your sleeves attitude to deliver projects under tight schedule.
• Great attention to details.

Responsibilities

• Assess current-state architecture across research and production environments for strength and weakness. Gather consensus across teams to define areas to be improved, including data, computing, workflow orchestration, tooling, and integration patterns. Establish architecture principles and standards to promote best practices. Develop a pragmatic modernization roadmap that balances innovation, continued operation robustness, and incremental delivery. Define and champion best practices around code and model promotion from research to production workflows, including testing, validation, and release management automation.
• Evaluate and introduce new technologies, industry standard tools, and frameworks with a strong focus on operational robustness, observability, and traceability. Lead and contribute to the refactoring of existing architecture software components.
• Identify opportunities to apply AI agents, automation, and modern infrastructure to improve quantitative research and development productivity and quality, including research pipeline tooling (signal development, efficacy evaluation/comparison) and model lifecycle management (model implementation, monitoring, deployment)
• Lead and significantly contribute to the development work to implement, test and deploy modernized quantitative platforms. Lead the effort to migrate existing quant systems to the new platform while maintaining smooth daily investment operations.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal