Senior Machine Learning Engineer

Voleon is a technology company that applies state-of-the-art machine learning techniques to real-world problems in finance. As a Senior Machine Learning Engineer, you will partner directly with research staff to advance quantitative trading strategies by translating research ideas into production-quality code and maintaining data pipelines and modeling infrastructure.

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
• 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