Senior Software Engineering, Machine Learning Operations, Tapestry (AI)

Location: Mountain View, California, United States; flexible hybrid work model

Salary: $166,000–$244,000 base salary per year, plus bonus, equity, and benefits

Company

Tapestry is an Alphabet-backed group within Google, originally founded at X, building AI-powered tools for a cleaner, more reliable, and efficient electric grid.

What you will do

• Design, build, and maintain CI/CD pipelines for machine learning workflows using Cloud Build or GitHub Actions.
• Deploy and operate machine learning models in production with Vertex AI and GKE, focusing on scalability and high availability.
• Develop automated workflows for model training and batch prediction with Vertex AI Pipelines.
• Containerize training code with Docker and optimize it for cloud execution.
• Collaborate with machine learning engineers, data scientists, software engineers, and AI researchers across multimodal machine learning, information retrieval, natural language processing, and agentic AI.

Requirements

• Bachelor’s or master’s degree in computer science, engineering, or a related field.
• 3+ years of professional experience in software engineering, DevOps, or data engineering, including 1–2 years in MLOps or ML infrastructure.
• Strong proficiency in Python.
• Deep understanding of Docker and basic familiarity with container orchestration.
• Experience with a public cloud platform such as GCP, AWS, or Azure.
• Experience with Git, CI/CD, and artifact management.

Nice to have

• Hands-on experience with the GCP AI/ML stack, including Vertex AI, BigQuery, Feature Store, and Model Registry.
• Experience designing complex Kubeflow DAGs.
• Production-grade Terraform module development and maintenance.
• Familiarity with TensorFlow, PyTorch, or Scikit-learn.

Culture & Benefits

• Flexible hybrid work model.
• Competitive salary, equity, and bonus.
• Medical, dental, and vision coverage.
• Generous paid time off and 401(k) with employer contribution.
• Professional development opportunities and work on real-world energy infrastructure challenges.