Senior Lead Machine Learning Engineer

Responsibilities

• Design and deploy machine learning models and components to address real business needs, collaborating with Product and Data Science teams.
• Make informed ML infrastructure decisions based on modeling techniques, including model type, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation.
• Tackle complex problems by writing and testing production code, building and validating models, and automating tests and deployment.
• Collaborate in a cross functional Agile team to build and improve software powering advanced big data and ML workloads.
• Retrain, maintain, and monitor models in production to sustain performance.
• Leverage or develop cloud based architectures and platforms to deliver scalable ML models.
• Build efficient data pipelines to feed ML models.
• Apply CI/CD best practices, including test automation and monitoring, to enable reliable deployment of ML models and code.
• Maintain secure, well governed code and models, following Responsible and Explainable AI practices.
• Proficiency with Python, Scala, or Java for implementation.

Requirements

• Bachelor’s Degree.
• 8+ years designing and building data-intensive solutions using distributed computing (internship experience not counted).
• 4+ years programming in Python, Scala, or Java.
• 3+ years building, scaling, and optimizing ML systems.
• 2+ years leading teams delivering ML solutions.

Technologies

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

Benefits

• Health benefits
• Financial benefits
• Performance-based incentives including cash bonuses and long term incentives

Preferred Qualifications

• Master's or doctoral degree in computer science, electrical engineering, mathematics, or a related field.
• Experience developing and deploying ML solutions on public clouds such as AWS, Azure, or Google Cloud.
• 4+ years of hands on experience with industry standard ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow).
• 3+ years writing performant, resilient, and maintainable code.
• 3+ years performing data gathering and preparation for ML models.
• 3+ years of people management experience.
• Contributions to the ML field through conference talks, papers, blogs, open source, or patents.
• 3+ years building production ready data pipelines that feed ML models.
• Ability to clearly communicate complex technical concepts to diverse audiences.