Machine Learning Engineer

This early career Machine Learning Engineer role supports the development and deployment of advanced machine learning models addressing complex challenges for the electric grid. The position involves work across several machine learning areas, including multimodal machine learning, information retrieval, natural language processing, and agentic AI.

Location and Work Model

Mountain View, CA (hybrid).

Compensation

USD 166,000 - 244,000 per year.

Education

Master’s Degree/Bachelor's Degree in Machine Learning, Computer Science, Statistics, or a related field.

Summary of the Role

As an early career Machine Learning Engineer, you will build and deploy state of the art machine learning models aimed at high-impact, real-world problems. Collaboration with senior team members is central, with exposure to multiple ML domains and enterprise-grade system development.

Responsibilities

• Train and deploy machine learning models in production environments.
• Partner with senior team members to develop enterprise quality ML systems across multiple machine learning domains.
• Operationalize machine learning model training and serving at enterprise scale.
• Keep current with the latest advancements in machine learning.

Requirements

• Master’s Degree/Bachelor's Degree in Machine Learning, Computer Science, Statistics, or a related field.
• Experience in machine learning model development and engineering.
• Expertise in one or more areas including multimodal machine learning, NLP, agentic AI, planning, control, or reinforcement learning.
• Strong programming skills in Python and experience with ML frameworks such as PyTorch or TensorFlow.
• Experience building and deploying ML systems at scale, or a proven ability to perform applied ML research and develop state of the art results in an academic setting.

Technologies

• Python
• PyTorch
• TensorFlow
• AWS
• GCP
• Azure

Benefits

• Competitive salary and equity
• Medical, dental, and vision coverage
• Generous PTO and flexible hybrid work model
• 401(k) with employer contribution
• Professional development
• Opportunity to work on important real-world problems within an Alphabet-backed environment

Additional Qualifications (Preferred)

• PhD in Machine Learning, Computer Science, Statistics, or a related field
• Experience with cloud platforms such as AWS, GCP, or Azure
• A strong portfolio of projects demonstrating ML expertise

Our Values

• Take charge: take initiative and own outcomes that move the mission forward.
• Transform with purpose: build solutions that solve real problems and create meaningful impact.
• Be a Tapestry, not a thread: collaborate across diverse skills and perspectives to achieve more together.
• Always fine-tune: stay curious, seek feedback, and refine understanding as learning continues.
• Stay grounded: listen openly, value different perspectives, and stay focused on what matters most.