Senior Machine Learning Engineer

Role Overview

Responsibilities

• Partner with PhD researchers to design, implement, and productize machine learning models for quantitative trading strategies.
• Develop and maintain data pipelines for ingestion, feature engineering, validation, and quality monitoring.
• Translate research prototypes into performant, well-tested, production-ready code.
• Build extensible tools and frameworks that accelerate model development and experimentation.
• Investigate and remediate subtle data-quality issues across research and production environments.
• Lead projects from requirements through delivery, deciding scope, dependencies, and trade-offs with long-term maintainability in mind.
• Contribute to deployment efforts, guide junior engineers and researchers, and align stakeholders on ownership, execution, and prioritization.
• Promote 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.
• At least 5 years of professional software engineering experience with strong foundations in data structures, algorithms, and systems design.
• Mathematical maturity with statistics, linear algebra, optimization, and probability.
• Deep proficiency in Python.
• Extensive experience with numerical and data science libraries such as NumPy, Pandas, SciPy, scikit-learn, PyTorch, or TensorFlow.
• Experience building or maintaining machine learning systems in a distributed computing environment.
• Linux development proficiency with attention to performance, correctness, and reproducibility.
• Exceptional attention to detail with imperfect or heterogeneous data.
• Strong written and verbal communication and effective collaboration with research partners.

Preferred Qualifications

• Experience with R or C/C++ is a strong plus.
• Experience with experiment management, model evaluation pipelines, or ML workflow orchestration is preferred.
• Familiarity with model serving, feature stores, distributed training, or other modern ML/AI infrastructure patterns is preferred.
• Experience profiling and optimizing numerical or modeling code is preferred.
• Prior exposure to financial data, time-series analysis, or quantitative research environments is preferred.

Compensation

• Compensation range is $290K to $395K.