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

Alphabet-backed Tapestry develops AI-enabled analytical and planning tools for the electric grid and supports energy partners across the U.S., U.K., Chile, New Zealand, Australia, and Brazil.

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 environments such as Vertex AI and GKE, with a focus on scalability and high availability.
• Develop automated workflows for model training and batch prediction with Vertex AI Pipelines.
• Collaborate with AI researchers, data scientists, machine learning engineers, and software engineers across multimodal ML, information retrieval, NLP, and agentic AI.
• Containerize training code with Docker and optimize it for cloud execution.

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

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

Culture & Benefits

• Culture focused on growth, ownership, collaboration, continuous learning, and meaningful impact.
• Medical, dental, and vision coverage.
• Generous paid time off and flexible hybrid work.
• 401(k) with employer contribution.
• Professional development opportunities.
• Competitive salary, bonus, equity, and the opportunity to solve real-world energy problems in an Alphabet-backed environment.