ML Engineer: AI for the Electric Grid

Tapestryenergy; Mountain View (CA); On-site; USD 166,000 - 244,000; Full time

Benefits offered by this job

Competitive salary

Equity

Medical coverage

PTO

Flexible hybrid work

401(k)

Professional development

Job summary

Tapestry is seeking an early career Machine Learning Engineer to build and deploy state-of-the-art ML models for the AI-powered electric grid. You will collaborate with ML Engineers, Data Scientists, and Software Engineers across multimodal ML, NLP, and agentic AI domains.

You will train and deploy models in production, develop enterprise-quality ML systems, and stay current with ML advancements. The role offers a hybrid environment and exposure to high-impact infrastructure projects.

Qualifications

• Master’s or Bachelor’s in ML, CS, statistics or related field.
• 3+ years of ML model development and engineering.
• Expertise in multimodal ML, NLP, or agentic AI, planning, control, or reinforcement learning.
• Strong Python and ML framework experience (PyTorch or TensorFlow).
• Experience deploying ML systems at scale or conducting applied ML research.

Responsibilities

• Train and deploy machine learning models in production.
• Develop enterprise-quality ML systems across multiple ML domains.
• Operationalize ML model training and serving at enterprise scale.
• Stay abreast of the latest advancements in machine learning.

Skills

Python programming

ML model development

ML at scale

NLP

Reinforcement learning

Education

Master's or Bachelor's in ML, CS, Statistics or related field

Tools

PyTorch

TensorFlow

Job description

Tapestry is seeking an early career Machine Learning Engineer to build and deploy state-of-the-art ML models for the AI-powered electric grid. You will collaborate with ML Engineers, Data Scientists, and Software Engineers across multimodal ML, NLP, and agentic AI domains.

You will train and deploy models in production, develop enterprise-quality ML systems, and stay current with ML advancements. The role offers a hybrid environment and exposure to high-impact infrastructure projects.