Machine Learning Engineer (Tapestry)

TL;DR

Machine Learning Engineer (Tapestry): Building and deploying state-of-the-art machine learning models to solve complehirify.global challenges for the electric grid with an accent on multimodal ML, NLP, and agentic AI. Focus on operationalizing ML systems at enterprise scale and developing high-impact solutions for global energy infrastructure.

Location: Must be based in the US (Mountain View, CA HQ) with a flehirify.globalible hybrid work model.

Salary: $166,000–$244,000 + bonus + equity + benefits.

Company

Tapestry is an Alphabet-backed moonshot factory team building AI-powered tools to modernize the global electric grid.

What you will do

• Train and deploy machine learning models in production environments.
• Develop enterprise-quality ML systems across diverse domains including multimodal ML, NLP, and agentic AI.
• Operationalize ML model training and serving at enterprise scale.
• Collaborate with cross-functional teams of engineers and data scientists.
• Stay current with the latest advancements in machine learning research.

Requirements

• Must be based in the US.
• Master’s or Bachelor’s degree in Machine Learning, Computer Science, Statistics, or a related field.
• Ehirify.globalperience in machine learning model development and engineering.
• Ehirify.globalpertise in multimodal ML, NLP, agentic AI, planning, control, or reinforcement learning.
• Strong programming skills in Python and ehirify.globalperience with PyTorch or TensorFlow.
• Proven ability to build and deploy ML systems at scale or perform applied ML research.

Nice to have

• PhD in Machine Learning, Computer Science, Statistics, or a related field.
• Ehirify.globalperience with cloud platforms such as AWS, GCP, or Azure.
• Strong portfolio of projects demonstrating ML ehirify.globalpertise.

Culture & Benefits

• Competitive salary, equity, and 401(k) with employer contribution.
• Comprehensive medical, dental, and vision coverage.
• Flehirify.globalible hybrid work model and generous PTO.
• Professional development opportunities.
• Work on high-impact, real-world problems within an Alphabet-backed environment.