Machine Learning Engineer 5 (Senior Manager, IC)

Design, build, and scale production-grade machine learning with a role focused on training and serving platforms, model monitoring, and Responsible and Explainable AI. This onsite position in New York, NY pairs hands-on ML engineering with platform and infrastructure work, including CI/CD, data pipelines, and cloud architectures that support large-footprint model training and deployment.

What you’ll do

• Design, build, and deliver ML models and components to solve real business problems in close collaboration with Product and Data Science.
• Build and scale massive multi-tenant platforms for large footprint ML training and serving.
• Drive ML infrastructure decisions using modeling expertise across model and data selection, feature selection, training, hyperparameter tuning, dimensionality, validation, and bias/variance tradeoffs.
• Solve complex engineering problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Collaborate in a cross-functional Agile environment to create and enhance software for big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage and/or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Apply continuous integration and continuous deployment practices, including test automation and monitoring, to support successful releases.
• Ensure well-managed code to reduce vulnerabilities, support risk-governed model governance, and follow best practices in Responsible and Explainable AI.
• Use programming languages such as Python, Scala, or Java.

Required qualifications

• 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 Machine Learning experience with industry standard frameworks PyTorch or TensorFlow and libraries Pandas, NumPy, Scikit-learn.
• At least 6 years experience operating large-scale distributed systems such as Spark and Ray to prepare AI/ML data.
• At least 4 years deploying and operating ML solutions in production and operating production services in the cloud (AWS, GCP, Azure), using Kubernetes to manage large-scale containerized ML software systems.

Preferred qualifications

• Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or a 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 solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plan preparation.
• 5+ years working with ML techniques (Supervised, semi-supervised, unsupervised, reinforcement learning) across model types (Regression, Classification, Clustering), architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and evaluating model accuracy to diagnose underfitting and overfitting.
• 5+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.
• ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.
• Ability to communicate complex technical and ML concepts clearly to a variety of audiences.

Technologies

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

Salary: USD 229,900 - 262,400 per year. Experience: minimum 4 years. Location: New York, NY (onsite).