part of an Agile team dedicated to productionizing machine learning applications and systems at scale
participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms
focus on machine learning architectural design
develop and review model and application code
ensure high availability and performance of machine learning applications
continuously learn and apply the latest innovations and best practices in machine learning engineering
design, build, and deliver ML models and components that solve real-world business problems
inform ML infrastructure decisions using understanding of ML modeling techniques and issues
solve complex problems by writing and testing application code, developing and validating ML models
collaborate as part of a cross-functional Agile team
retrain, maintain, and monitor models in production
leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
construct optimized data pipelines to feed ML models
leverage continuous integration and continuous deployment best practices to ensure successful deployment

Bachelor’s Degree
At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
At least 4 years of experience programming with Python, Scala, or Java
At least 2 years of experience building, scaling, and optimizing ML systems
Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field (preferred)
3+ years of experience building production-ready data pipelines that feed ML models (preferred)
3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow (preferred)
2+ years of experience developing performant, resilient, and maintainable code (preferred)
2+ years of experience with data gathering and preparation for ML models (preferred)
1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation (preferred)
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform (preferred)
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance (preferred)
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents (preferred)
Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion (preferred)

comprehensive, competitive, and inclusive set of health, financial and other benefits that support total well-being

Lead Machine Learning Engineer

Key skills