Software Engineer - Applied AI Research
About The Position
The role is a senior-level Applied AI / Machine Learning Software Engineer position focused on building the engineering foundation for next-generation quantitative investment workflows. At the intersection of AI/ML, quantitative finance, and high-scale systems architecture, the engineer will translate research concepts into resilient, high-throughput production systems that support live trading and portfolio decision-making. The position involves conceptualizing, prototyping, and evaluating AI/ML models; engineering them into scalable, low-latency applications; and designing modular libraries, testing frameworks, and infrastructure for quantitative research and deployment. The engineer will embed AI solutions in Equities, Fixed Income, and Multi-Asset strategies, provide hands-on technical leadership, and collaborate with quantitative researchers, portfolio managers, and global engineering teams. The role may also involve representing the organization at industry conferences and contributing to open-source initiatives.
Requirements
- Master’s or PhD in Computer Science, Machine Learning, Mathematics, Statistics, Physics, Quantitative Finance, or a related quantitative discipline, or equivalent demonstrably deep industry experience.
- At least 5 years of software engineering experience building robust, production-grade applications in finance, quantitative technology, or related data-intensive domains.
- Exceptional programming skills in Python, C++, Java, or modern object-oriented or functional languages.
- Strong computer science fundamentals, including data structures, algorithms, and concurrency.
- Practical experience designing, training, evaluating, and deploying modern machine learning or deep learning systems.
- Hands-on experience developing software for quantitative investment workflows and a solid grasp of asset classes such as Equities, Fixed Income, or Multi-Asset.
- Commitment to clean architecture, automated testing, continuous integration, scalability, and maintainability.
Nice To Haves
- Demonstrated success taking novel ML/AI models from experimentation into high-availability production environments.
- Hands-on experience with financial time-series data, massive structured datasets, and portfolio analytics.
- Exposure to distributed computing, cloud architectures, or high-performance computing (HPC).
- Public engineering or research contributions, such as open-source work, technical blog posts, or conference papers.
Responsibilities
- Conceptualize, prototype, and rigorously evaluate state-of-the-art AI/ML models, then engineer them into scalable, low-latency production applications.
- Design and implement modular libraries, testing frameworks, and scalable infrastructure for the quantitative research and deployment lifecycle.
- Embed AI solutions into investment workflows across Equities, Fixed Income, and Multi-Asset strategies.
- Provide hands-on technical leadership, set code quality standards, and advocate for strong software engineering practices and system reliability.
- Collaborate with quantitative researchers, portfolio managers, and global engineering teams to address complex market challenges.
- Represent the organization at industry conferences and contribute to open-source initiatives where appropriate.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
