ashby
Senior Machine Learning Engineer
Voleon
Remote, USA
Full-time
Senior · 5+ years experience
Remote
USD 290000-395000 yearly / year
Discovered 1 weeks ago
PythonRC/C++NumPyPandasSciPy
Free
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PythonRC/C++
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PythonRC/C++NumPyPandasSciPy
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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
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