Note: The job is a remote job and is open to candidates in USA. 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
Skills
- 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
Benefits
- Highly competitive compensation and benefits packages
- Technology talks by our experts
- A beautiful modern office
- Catered lunches
Company Overview
Company H1B Sponsorship