Senior Manager, Machine Learning Engineer

About the job

Responsibilities

• Design, build, and deliver ML models and components to solve real-world business problems in collaboration with Product and Data Science teams.
• Build and scale multi-tenant platforms for large-scale ML model training and 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 governed for risk, and ML practices adhere to Responsible and Explainable AI principles.
• Use programming languages such as Python, Scala, or Java.

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, and Scikit-learn.
• At least 6 years of experience using and operating large-scale distributed systems (Spark, Ray) for AI/ML data preparation.
• At least 4 years of experience deploying and operating Machine Learning 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.
• Master's or doctoral degree in computer science, electrical engineering, mathematics, or a related field (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, and incident response planning (preferred).
• 5+ years of experience with Machine Learning techniques (Supervised, semi-supervised, unsupervised, reinforcement learning), model types (Regression, Classification, Clustering), model architectures (RNNs, CNNs, LSTMs, Transformers), training concepts, and model evaluation (preferred).
• 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models (preferred).
• Demonstrated ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents (preferred).
• Ability to communicate complex technical and machine learning concepts clearly to various audiences.

Benefits

• Performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI).