Manager, Machine Learning Operations will lead a team of engineers to design, deploy, and operate machine learning solutions while building scalable infrastructure and best practices across the Machine Learning Engineering ecosystem. The manager will oversee the full ML lifecycle, including building ETL pipelines, deploying models into customer-facing applications, and owning the roadmap for reusable frameworks and standardized tools. Key responsibilities include providing technical thought leadership, conducting code reviews, and developing monitoring, alerting, and automated testing frameworks. Required technical skills include expertise in Docker, Kubernetes, CI/CD pipelines, Git, and API development. The role requires proficiency in Python, TensorFlow, PyTorch, and scikit-learn, alongside experience with Google Cloud Platform services like Vertex AI, BigQuery, and Dataproc, as well as Spark, Kubeflow, and Airflow.
Manage a team of engineers to design, deploy, and operate production machine learning solutions.
Lead the implementation of the full ML lifecycle, including building and scaling ETL pipelines.
Own the roadmap for ML engineering tools and develop reusable frameworks to streamline model implementation.
Provide technical leadership to Data Science managers on cloud-based tools and infrastructure.
Develop and maintain monitoring, alerting, and automated testing frameworks for data pipelines and models.
Research and prototype cutting-edge machine learning infrastructure to improve organizational throughput.
Train and coach engineers through pair programming, code reviews, and the instillation of best practices.
Document and communicate ML implementations and best practices to leaders across the data organization.
What we're looking for
Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, or an equivalent quantitative field.
Master’s degree (preferred).
5+ years of experience as a Machine Learning Engineer with a proven track record of successful, independent, project delivery.
7+ years of experience in Machine Learning (preferred).
2+ years of managerial or leadership experience in Data Science or Analytics organizations.
Expertise in ML Ops practices including Docker, Kubernetes, CI/CD pipelines, Git, API development, model serving, and automated testing.
Extensive expertise in Python, machine learning libraries (TensorFlow, PyTorch, scikit-learn), and distributed computing technologies like Spark, Kubeflow, Airflow, and SQL.
Experience with cloud platforms, particularly Google Cloud Platform services like Vertex AI, BigQuery, and Dataproc.
Proficiency in Java or other languages (preferred).
Retail and E-Commerce experience (preferred).
Experience with optimization techniques (e.g., Gurobi) and agent-based or agentic AI systems (preferred).
Machine Learning
Computer Vision
Generative AI
PyTorch
TensorFlow
Deep Learning
Distributed Systems
Video Understanding
Multimodal Learning
System Design
Remote (Thousand Oaks, CA)3 days ago
$187,395–$253,534
Machine Learning
Generative AI
Python
LLMs
Retrieval-Augmented Generation
AWS
Terraform
CI/CD
APIs
Data Pipelines
Infrastructure as Code
Observability
Full-Stack Development
Version Control