Participate in 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, and ensure high availability and performance of ML applications.
Design, build, and/or 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, and automating tests and deployment.
Collaborate as part of a cross-functional Agile team.
Retrain, maintain, and monitor models in production.
Leverage or build cloud-based architectures.
Construct optimized data pipelines to feed ML models.
Leverage continuous integration and continuous deployment best practices.
Ensure all code is well-managed to reduce vulnerabilities and ML follows best practices in Responsible and Explainable AI.
Use programming languages like Python, Scala, or Java.

Requirements

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 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)
2+ years of people leader experience (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).

Benefits

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningML architectural designmodel developmentapplication code reviewdata pipelinesML modeling techniquesPythonScalaJavaML frameworks

Soft Skills

problem solvingcollaborationleadershipcommunicationteam development

Certifications

Bachelor's degreeMaster's degreeDoctoral degree