Senior Software Engineering, Machine Learning Operations

Tapestry•Mountain View, CA

3d•$166,000 - $244,000•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

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).

Responsibilities

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.