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
“FRIENDS OF VOLEON” CANDIDATE REFERRAL PROGRAM
If you have a great candidate in mind for this role and would like to have the potential to earn $15,000 if your referred candidate is successfully hired and employed by The Voleon Group, please use this form https://voleon.com/referrals/ to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program https://voleon.com/assets/Voleon-Referral-Bonus-Program.pdf.
EQUAL OPPORTUNITY EMPLOYER
The Voleon Group is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.
