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 $232 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 seeking a Principal Quant Engineer to work in close collaboration with Research, Portfolio Management, and Data teams to design and implement scalable, production-grade platforms that support Acadian’s quantitative research and investment processes.

This role focuses on building quant tooling that enables researchers to efficiently develop, evaluate, and deploy quantitative models across investment processes including alpha, risk, transaction cost analysis, attribution, etc. The emphasis is on creating infrastructure that makes quantitative research repeatable, observable, and production-ready at scale.

Acadian supports a hybrid work environment; employees are on-site in the Boston office 3 days a week.

What You’ll Do:

• Design and implement quant tooling that supports signal construction and evaluation workflows, powering production portfolios across global asset universes.
• Work closely with quantitative researchers to ensure research infrastructure continues to evolve in step with their needs, enabling efficient experimentation and reliable alpha generation.
• Lead the AI centric development and evolution of core platform capabilities that support signal computation, data access, workflow orchestration, and distributed execution for both quantitative signals and machine learning-based techniques, including deep learning workflows built with PyTorch.
• Ensure platform flexibility while maintaining integration with downstream processes for portfolio construction, portfolio management, marketing, client reporting, etc
• Incorporate machine learning and AI techniques into signal construction, parameter estimation, and efficacy evaluation workflows where they improve forecast quality and research productivity, including hyper‑parameter optimization using frameworks such as Optuna or similar.