Build and scale production-grade machine learning systems across modeling, data pipelines, and deployment practices.

Design, build, and/or deliver ML models and components to address real-world business needs in collaboration with Product and Data Science
Develop and scale large multi-tenant platforms for ML model training and/or serving at scale
Guide ML infrastructure decisions using ML modeling expertise, including model and feature selection, training, hyperparameter tuning, dimensionality considerations, bias-variance tradeoffs, and validation
Solve complex problems through application code development, ML model development and validation, plus automated testing and deployment
Work in a cross-functional Agile team to create and enhance software supporting big data and ML applications
Retrain, maintain, and monitor machine learning 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 CI/CD best practices, including test automation and monitoring, to support successful deployment of ML models and application code
Manage code to reduce vulnerabilities, govern models from a risk perspective, and follow best practices for Responsible and Explainable AI
Use Python, Scala, or Java to implement ML and platform components

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 of programming experience with Python, Java, Golang, or C++
At least 6 years of ML experience with industry-standard frameworks PyTorch or Tensorflow, plus libraries (Pandas, NumPy, Scikit-learn)
At least 6 years operating large-scale distributed systems (e.g., Spark, Ray) to prepare AI/ML data
At least 4 years deploying and operating ML solutions in production and running production services in the cloud (AWS, GCP, Azure), using Kubernetes for large-scale containerized ML systems

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 (source control, testing, code reviews, 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 applying ML techniques (supervised, semi-supervised, unsupervised, reinforcement learning) across model types (Regression, Classification, Clustering), architectures (RNNs, CNNs, LSTMs, Transformers), and training concepts (loss function, hyperparameters, regularization), plus evaluating accuracy and diagnosing issues (underfitting, 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 varied audiences

Location: New York, NY (onsite)

Salary: USD 229,900 - 262,400 per yearly

Minimum experience: 4 years

Machine Learning Engineer 5 (Senior Manager, IC)

Job Description