Machine Learning Engineer 5 (Senior Manager, IC)

What you’ll do

• Design, build, and deliver ML models and components that solve real business problems in partnership with Product and Data Science.
• Build and scale massive multi-tenant platforms for large-footprint model training and/or serving.
• Shape ML infrastructure decisions using expertise in modeling and training considerations, including choice of model, data and feature selection, training approaches, hyperparameter tuning, dimensionality, bias/variance, 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 to support continued performance.
• Leverage and/or build cloud-based architectures and platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Apply CI/CD best practices with test automation and monitoring to improve deployment success for ML models and application code.
• Ensure secure, well-managed code and strong governance from a risk perspective, aligning with Responsible and Explainable AI best practices.
• Use programming languages including Python, Scala, or Java.

What you bring

• Bachelor’s Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering).
• At least 6 years programming with Python, Java, Golang, or C++.
• At least 6 years of Machine Learning experience with PyTorch or TensorFlow, plus libraries such as Pandas, NumPy, and Scikit-learn.
• At least 6 years operating large scale distributed systems like Spark and Ray to prepare AI/ML data.
• At least 4 years deploying and operating ML solutions in production, including cloud services such as AWS, GCP, or Azure, and using Kubernetes to manage large-scale containerized ML systems.

Technologies

Python, Scala, Java, Golang, C++, PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn, Spark, Ray, AWS, GCP, Azure, Kubernetes

Preferred qualifications

• Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field.
• 5+ years optimizing ML algorithms, configurations, and infrastructure.
• 5+ years following software development best practices including source control, testing, code reviews, and CI/CD.
• 5+ years building resilient software with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plan preparation.
• 5+ years experience with ML techniques (Supervised, semi-supervised, unsupervised, reinforcement learning) and model types (Regression, Classification, Clustering), architectures (RNNs, CNNs, LSTMs, Transformers), and training concepts (loss function, hyperparameters, regularization), including diagnosing underfitting/overfitting.
• 5+ years designing, implementing, and scaling production-ready data pipelines for training and evaluation.
• ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.
• Ability to communicate complex technical and ML concepts clearly to varied audiences.