Job Description

Senior Machine Learning Engineer

Onsite 4 days a week- Phoenix, AZ

$130-160,000 + Bonus

About the Company:

My client is developing a groundbreaking forecasting engine for nuclear equipment performance. At its core is a novel approach to interpreting historical maintenance and operations logs, turning them into intelligent diagnostics and predictive insights. The goal? Equip engineers with advanced tools that help predict and understand performance issues in high-stakes environments like power plants.

This is a research-heavy role with a lean, collaborative team, focusing on building custom neural net architectures from scratch. The work blends deep learning, diagnostics, forecasting, and multimodal data modeling.

The Role:

As a Machine Learning Engineer , you’ll play a central role in designing and building the core of this next-generation forecasting system. This is not your typical MLE position—you’ll be expected to contribute original research, prototype novel architecture s , and build foundational models to help bridge performance data with human decision-making in complex mechanical systems.

Key Responsibilities:

• Design custom neural network architectures, including modifications to language models
• Build a multimodal model that integrates text, time series, and other sensor data to forecast equipment behavior
• Conduct literature reviews, define research approaches, and lead early experiments
• Deliver high-quality, production-ready ML code for deployment
• Collaborate closely with software engineers, analysts, and domain experts

What You'll Need to Succeed:

• Strong background in language models, multimodal learning, and time series forecasting
• Deep experience with PyTorch and LLM architecture design
• Track record of self-driven ML experiments, research publications, or hobby neural net projects
• Understanding of data science best practices, including training/validation, feature engineering, and model evaluation
• Experience working in cloud environments such as AWS, GCP, or Azure
• Bonus: Familiarity with Docker, Linux, or DevOps principles

Why Join This Team?

• Work on a meaningful challenge that brings cutting-edge machine learning to critical infrastructure
• Collaborate in a small, fast-moving team where your ideas directly shape the product
• Apply real research to production—not just polishing existing models, but building the architecture from the ground up
• A rare opportunity to work on LLMs and multimodal modeling with real-world impact
• Competitive compensation and the chance to help define the future of predictive diagnostics in high-tech environments

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