About Voleon
Voleon is a technology company that applies state-of-the-art machine learning techniques to real-world problems in finance. For more than a decade, we have led our industry and worked at the frontier of applying machine learning to investment management. We have become a multibillion-dollar asset manager, and we have ambitious goals for the future.
In addition to our enriching and collegial working environment, we offer highly competitive compensation and benefits packages, technology talks by our experts, a beautiful modern office, catered lunches, and more.
About the Role
As a Senior Machine Learning Engineer on one of Voleon's Research teams, you will partner directly with research staff to advance our quantitative trading strategies. You will translate novel research ideas into production-quality code, build and maintain the data pipelines and modeling infrastructure that underpin our strategies, and apply your own strong mathematical intuition to solve open-ended technical challenges.
This role lives at the boundary of research and engineering. You will be expected to understand the statistical and mathematical concepts your research partners work with, contribute meaningfully to technical discussions about model design and evaluation, and ensure that the resulting systems are performant, reliable, and maintainable. You will work at the intersection of Computer Science, Mathematics, and Statistics — building high-performance tools that enable world-class research while maintaining a high engineering standard.
Responsibilities
- Partner with PhD researchers to design, implement, and productize machine learning models that drive quantitative trading strategies
- Develop and maintain complex data pipelines, including data ingestion, feature engineering, validation, and quality monitoring
- Translate research prototypes and novel ideas into performant, well-tested, production-ready code
- Build extensible tools and frameworks that accelerate the model development and experimentation lifecycle
- Supervise, understand, and remediate subtle data quality issues across both research and production environments
- Proactively lead projects from requirements through delivery, making autonomous decisions about scope, dependencies, and trade-offs, with an emphasis on long-term maintainability
- Coordinate and contribute to deployment efforts while guiding junior engineers and researchers; align with research and engineering stakeholders on ownership, execution, and prioritization
- Foster engineering consistency, standards, and best practices within Research
Requirements
- Bachelor's degree (or higher) in Computer Science, Applied Mathematics, Statistics, or a related quantitative field
- 5+ years of professional software engineering experience, with strong CS fundamentals (data structures, algorithms, systems design)
- Demonstrated mathematical maturity — comfort with the concepts and notation used in statistics, linear algebra, optimization, and probability
- Deep proficiency in Python; experience with R and/or C/C++ is a strong plus
- Extensive experience with numerical and data science libraries (e.g., NumPy, Pandas, SciPy, scikit-learn, PyTorch, TensorFlow, or similar)
- Proven experience building or maintaining machine learning systems in a distributed computing environment
- Proficiency developing in a Linux environment with attention to performance, correctness, and reproducibility
- Exceptional attention to detail, particularly when working with imperfect or heterogeneous data
- Strong verbal and written communication skills, and the ability to collaborate effectively with researchers whose primary expertise is not software engineering
Preferred Qualifications
- Experience with experiment management, model evaluation pipelines, or ML workflow orchestration
- Familiarity with modern ML/AI infrastructure patterns (model serving, feature stores, distributed training)
- Experience with performance profiling and optimization of numerical or modeling code
- Prior exposure to financial data, time-series analysis, or quantitative research environments