Software Engineering, Machine Learning Operations

Tapestry is Alphabet’s moonshot for the electric grid, working at the frontier where energy’s complexity meets AI’s potential.

We were born at X, the innovation lab responsible for breakthrough technologies like Waymo, Verily and Google Brain.

To keep pace with humanity’s growing energy needs, the world needs a grid that is visible and understandable.

We provide that clarity by building advanced, AI-enabled analytical and planning tools that allow the entire energy ecosystem to plan smarter, move faster, and operate more efficiently—ensuring electricity remains reliable and affordable for everyone.

This is a global effort.

Tapestry is proud to support partners in the U.S., U.K., Chile, New Zealand, Australia and Brazil as they build a cleaner, more resilient energy future.

Joining Tapestry allows you to do the best work of your life as part of a multidisciplinary team of experts in AI, energy systems, software engineering and product design—all collaborating to reshape energy on a global scale.

If you want to tackle problems that matter and build tools with real impact, we would love to meet you.

Learn more about our team and our mission here .

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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