ML Engineer — Production AI for Smart Grids (Hybrid)

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

Benefits offered by this job

Competitive salary and equity

Medical, dental, and vision coverage

Generous PTO and flexible hybrid work

401(k) with employer contribution

Job summary

X, based in Mountain View, CA, 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 work with ML Engineers, Data Scientists and Software Engineers across multimodal ML, NLP and agentic AI to solve real-world infrastructure challenges.

We value hands-on ML model development, scalable deployment, and staying ahead of research trends.

Qualifications

• Master’s or Bachelor’s degree in ML, CS, Statistics or related field.
• Experience in ML model development and engineering.
• Expertise in multimodal ML, NLP or agentic AI, planning, control and reinforcement learning.
• Strong programming in Python with PyTorch or TensorFlow.
• Experience deploying ML systems at scale or performing applied ML research.

Responsibilities

• Train and deploy machine learning models in production environments.
• Collaborate with senior team members to build enterprise-quality ML systems across domains.
• Operationalize ML model training and serving at enterprise scale.
• Stay up-to-date with the latest advancements in machine learning.

Skills

Python programming

ML model development

Multimodal ML / NLP / agentic AI

Enterprise ML deployment

Education

Master’s or Bachelor’s in ML/CS/Statistics

Tools

PyTorch

TensorFlow

Job description

X, based in Mountain View, CA, 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 work with ML Engineers, Data Scientists and Software Engineers across multimodal ML, NLP and agentic AI to solve real-world infrastructure challenges.

We value hands-on ML model development, scalable deployment, and staying ahead of research trends.