About the Role:
We are looking for an experienced Applied AI / Machine Learning Software Engineer to join a high-performing team working at the
intersection of artificial intelligence, software engineering, and quantitative investment.
The successful candidate will collaborate with research and engineering teams to design, develop, and productionize advanced
machine-learning systems and applications supporting quantitative investment workflows.
This is a senior-level role combining AI/ML research, software engineering, quantitative finance, and technical leadership.
Key Responsibilities:
Collaborate with research and engineering colleagues to advance production-grade machine-learning systems and applications.
Conceptualize, prototype, experiment with, and evaluate AI/ML-based software solutions.
Translate research concepts and experimental results into reliable, scalable production systems.
Develop, test, and maintain high-quality, production-ready software.
Design and implement reusable libraries, frameworks, and infrastructure supporting reliable and testable systems.
Contribute to quantitative investment workflows across areas such as equities, fixed income, or multi-asset strategies.
Take ownership of technically complex, cross-team initiatives and provide technical leadership.
Work closely with quantitative researchers, engineers, and other stakeholders to solve complex business and technical problems.
Contribute to engineering best practices, code quality, testing, and system reliability.
Represent the organization at relevant industry conferences and contribute to open-source communities where appropriate.
Requirements:
- Master's or PhD degree in Computer Science, Machine Learning, Mathematics, Statistics, Physics, Engineering, Quantitative
Finance, or a related discipline; equivalent relevant industry experience will also be considered.
- 5+ years of professional industry experience in software engineering, machine learning, quantitative technology, or a closely
related field.
Strong software-development experience in Python, C++, Java, or comparable modern programming languages.
Extensive experience developing software supporting quantitative investment workflows.
- Strong understanding of one or more investment areas, such as:
- Equities
- Fixed Income
- Multi-Asset Strategies
Practical experience designing and implementing machine-learning or AI systems.
Strong understanding of software engineering principles, including testing, maintainability, scalability, and reliability.
Experience building reusable libraries, frameworks, or platforms.
Ability to work effectively across research, engineering, and business teams.
Demonstrated ability to take ownership of complex technical projects and provide technical leadership.
Preferred Experience:
Experience taking ML/AI solutions from research or experimentation into production.
Experience with quantitative research, portfolio analytics, financial modelling, or investment technology.
Experience working with financial time-series data and large-scale datasets.
Experience with distributed computing, cloud technologies, or high-performance computing.
Contributions to open-source projects, technical publications, or presentations at industry conferences.
Experience working in a highly collaborative, research-driven engineering environment.
Ideal Candidate Profile:
The ideal candidate combines strong software engineering fundamentals with practical AI/ML expertise and a solid understanding of
quantitative investment.
They are comfortable moving between research and production engineering, can independently lead technically complex initiatives,
and have a track record of building robust systems that are used by quantitative researchers or investment professionals.