Lead Machine Learning Engineer

This on-site lead machine learning engineering role in McLean, VA centers on productionizing ML applications at scale, with a yearly compensation range of USD 197,300 to 225,100.

Responsibilities

• Develop and deliver ML models and components that tackle real business needs, collaborating closely with Product and Data Science teams.
• Make informed choices about ML infrastructure, including model type, data selection, feature engineering, training, hyperparameters, and model validation to address modeling challenges.
• Tackle complex problems by writing robust application code, validating ML models, and automating testing and deployment processes.
• Work within a cross-functional Agile team to enhance software that supports cutting edge big data and ML applications.
• Retrain, monitor, and maintain models in production environments.
• Utilize or build cloud-based architectures and platforms to deliver scalable ML models efficiently.
• Design and optimize data pipelines that feed ML models.
• Adopt continuous integration and continuous deployment practices, including test automation and monitoring, to ensure reliable deployment of models and code.
• Maintain code quality and governance to mitigate risk and align ML work with responsible and explainable AI practices.
• Program primarily in Python, Scala, or Java.

Requirements

• Bachelor’s Degree
• Minimum of 6 years designing and building data-intensive solutions in distributed computing environments (internship experience not applicable)
• At least 4 years of programming experience in Python, Scala, or Java
• At least 2 years of experience building, scaling, and optimizing ML systems

Technologies

• Python, Scala, Java
• scikit-learn, PyTorch, Dask, Spark, TensorFlow
• AWS, Azure, Google Cloud Platform

Benefits

• Health benefits
• Financial benefits
• Other benefits
• Performance-based incentive compensation (cash bonuses and/or long-term incentives)

Preferred Qualifications

• Master’s or Doctoral degree in computer science, electrical engineering, mathematics, or a related field
• 3+ years building production-ready data pipelines that feed ML models
• 3+ years applying industry standard ML frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
• 2+ years creating performant, resilient, and maintainable code
• 2+ years gathering and preparing data for ML models
• 2+ years of people leadership experience
• 1+ year leading teams developing ML solutions with established patterns, automation, and best practices
• Experience deploying ML solutions in public cloud platforms (AWS, Azure, or Google Cloud)
• Experience designing, implementing, and scaling complex ML data pipelines and evaluating their performance
• Impact in the ML field through conferences, papers, blogs, open source work, or patents
• Experience using interactive AI tools to boost productivity beyond basic code completion