Senior Manager, Machine Learning Engineer (People Leader)

About the job

Responsibilities

• Design, build, and deliver ML models and components to solve business problems in collaboration with Product and Data Science teams.
• Build and scale multi-tenant platforms for large-footprint ML model training and/or serving.
• Inform ML infrastructure decisions based on understanding of ML modeling techniques, including model choice, data and feature selection, training, hyperparameter tuning, and validation.
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Collaborate within a cross-functional Agile team to create and enhance software for big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud-based architectures and platforms for optimized ML model delivery at scale.
• Construct optimized data pipelines to feed ML models.
• Apply continuous integration and continuous deployment (CI/CD) best practices, including test automation and monitoring, for successful deployment of ML models and application code.
• Ensure code is well-managed for reduced vulnerabilities, models are risk-governed, and ML practices follow Responsible and Explainable AI guidelines.
• Use programming languages such as Python, Scala, or Java.
• Act as a force multiplier by balancing technical contribution with mentoring and elevating team skills.
• Recruit, nurture, and retain top engineering talent.
• Share passion for staying current with tech trends, experimenting with new technologies, and participating in technology communities.

Requirements

• Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field.
• At least 6 years of programming experience with Python, Java, Golang, or C++.
• At least 6 years of Machine Learning experience using industry-standard frameworks like PyTorch or Tensorflow and libraries such as Pandas, NumPy, Scikit-learn.
• At least 6 years of experience using and operating large-scale distributed systems (Spark, Ray) for AI or Machine Learning data preparation.
• At least 4 years of experience deploying and operating ML solutions in production and production services in the cloud (AWS, GCP, Azure).
• At least 4 years of experience using Kubernetes to manage large-scale containerized ML software systems.
• At least 1 year of people leadership experience.
• Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field (preferred).
• 3+ years of experience managing and leading an ML engineering team (preferred).
• 5+ years of experience optimizing ML algorithms, configurations, and infrastructure (preferred).
• 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD (preferred).
• 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans (preferred).
• 5+ years of experience with various ML techniques, model types, architectures, training concepts, and evaluation methods (preferred).
• 5+ years of experience designing, implementing, and scaling production-ready data pipelines for ML models (preferred).
• Demonstrated ML industry impact through conference presentations, papers, blog posts, open-source contributions, or patents (preferred).

Benefits

• Visa sponsorship available for qualified applicants.