Machine Learning Engineer 5 (Senior Manager, IC)

Information Technology Senior Management Forum; New York (NY); On-site; USD 230,000 - 262,000; Full time

Job summary

Information Technology Senior Management Forum in New York, NY seeks a senior ML systems engineer to design, build, and scale production-grade ML models and platforms across modeling, data pipelines, and deployment practices.

You will collaborate with Product and Data Science, scale multi-tenant training/serving platforms, and apply CI/CD, cloud architectures (AWS/GCP/Azure), and Kubernetes for production-ready ML systems.

Qualifications

• Bachelor’s degree or higher in Computer Science, ML, or related quantitative field.
• At least 6 years of programming with Python, Java, Golang, or C++.
• At least 6 years of ML with PyTorch or TensorFlow and libraries (Pandas, NumPy, Scikit-learn).
• At least 6 years operating large-scale distributed systems (e.g., Spark, Ray).
• At least 4 years deploying and operating ML in production and cloud environments (AWS, GCP, Azure) using Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science.
• Develop and scale large multi-tenant platforms for ML training and serving.
• Guide ML infrastructure decisions: model/feature selection, training, tuning, validation.
• Develop code for ML/model deployment and automated testing.
• Work in a cross-functional Agile team to support big data and ML apps.
• Retrain, maintain, and monitor ML models in production.
• Leverage cloud architectures to deliver optimized ML models at scale.
• Construct optimized data pipelines for ML models.
• Apply CI/CD, test automation, and monitoring for ML deployments.
• Govern models for risk and apply Responsible AI practices.
• Use Python, Scala, or Java to implement ML/platform components.

Skills

ML modeling

Cloud computing

Distributed systems

Agile teamwork

Communication

Education

Bachelor's degree or higher in CS or related field

Tools

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

AWS

GCP

Azure

Job description

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

Responsibilities

• 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

Requirements

• 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

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 (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