Senior Software Engineering, Machine Learning Operations, Tapestry

About Tapestry

Tapestry is a group within Google working to build the AI-powered electric grid. We are tackling one of the world’s most important infrastructure challenges: helping the energy system become more visible, understandable, reliable, affordable, abundant, and clean.

Originally born at X, Alphabet’s moonshot factory, Tapestry brings together experts in energy, AI, software engineering, and products to build tools that help the electricity ecosystem plan smarter, move faster, and operate more efficiently.

This is a global effort. Tapestry supports partners across the U.S., U.K., Chile, New Zealand, Australia, and Brazil as they work toward a cleaner, more resilient energy future.

Joining Tapestry means doing high-impact work with a multidisciplinary team tackling a problem that matters at global scale. Learn more about our team and our mission here.

About the role:

We're looking for an early career engineer to join our Machine Learning team. In this role you will help build and deploy state of the art machine learning models to solve complex challenges that face today’s electric grid. You will work closely with other Machine Learning Engineers, Data Scientists and Software Engineers across diverse ML domains spanning multimodal machine learning, information retrieval, natural language processing and agentic AI.

How you will make 10x impact:

• Design, build, and maintain CI/CD pipelines for Machine Learning workflows using tools like Cloud Build or GitHub Actions.
• Manage the deployment of ML models into production environments (e.g., Vertex AI, GKE), focusing on scalability and high availability..
• Develop and manage automated ML workflows for training and batch prediction using tools Vertex AI Pipelines.
• Work closely with AI Researchers and Data Scientists to containerize training code (Docker) and optimize code for cloud execution, bridging the gap between experimentation and production.

What you should have:

• Master’s Degree/Bachelor's Degree in Computer Science, Engineering or related field
• 3+ years of professional experience in Software Engineering, DevOps, or Data Engineering, with at least 1-2 years focused specifically on MLOps or ML infrastructure.
• Strong proficiency in Python 
• Deep understanding of Docker and basic familiarity with container orchestration.
• Experience working with public cloud platforms (GCP, AWS, or Azure).
• Experience with version control (Git), CI/CD, and artifact management.

It’d be great if you also had these:

• GCP Specialization: Hands-on experience specifically with the GCP AI/ML stack, including Vertex AI (Pipelines, Feature Store, Model Registry), BigQuery.
• Orchestration: Experience designing complex DAGs using Kubeflow.
• Infrastructure as Code: Strong experience writing and maintaining production-grade Terraform modules.
• ML Frameworks: Familiarity with standard ML frameworks (TensorFlow, PyTorch, Scikit-learn).

Our values

• Take charge: We take initiative and own outcomes that move the mission forward.
• Transform with purpose: We build solutions that solve real problems and create meaningful impact.
• Be a Tapestry, not a thread: We collaborate across diverse skills and perspectives to achieve more than we can individually.
• Always fine-tune: We stay curious, seek feedback, and refine our understanding as we learn.
• Stay grounded: We listen openly, value different perspectives, and stay focused on what matters most.

What we offer

A culture that supports growth, ownership, and meaningful impact, along with:

• Competitive salary and equity
• Medical, dental, and vision coverage
• Generous PTO and flexible hybrid work model
• 401(k) with employer contribution
• Professional development
• The ability to work on important real-world problems within an Alphabet-backed environment

The US base salary range for this full-time position is $166,000 - $244,000 + bonus + equity + benefits. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.

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