Machine Learning Engineer

Machine Learning Engineer

About Themis Intelligence

Themis Intelligence builds the Utility Knowledge Base (UKB) and Human-Guided Intelligence (HGI) platforms, redefining how utilities operate.

Our systems transform complex operational data into clear, high-confidence decisions. We design software that empowers grid professionals to think faster, act decisively, and operate with precision in critical environments. Every product we ship is built for real-world performance: reliable, observable, and secure from day one.

About the Role

As a Machine Learning Engineer, you will contribute to the development of advanced intelligence systems that power modern utility operations. We work at the frontier of applied AI, building models and data systems that integrate time-series data, geospatial signals, and scalable infrastructure to support critical grid environments.

This role goes beyond experimentation. You will work across the full lifecycle of machine learning systems, contributing to architecture decisions, implementing production-grade pipelines, and deploying models through mature MLOps practices across both cloud and on-premises environments. We emphasize evidence-based development, benchmark validation, and operational reliability from day one.

In this role, you will

Develop and deploy machine learning and deep learning models for time-series forecasting, anomaly detection, and geospatial intelligence

Contribute to the design of ML system architecture, ensuring scalability, reproducibility, and long-term maintainability

Build and maintain end-to-end MLOps pipelines, including data ingestion, training workflows, validation, model registry, CI/CD integration, and monitoring

Deploy and support models across cloud-native and on-premises infrastructure with production-grade reliability

Work with incomplete, noisy, and large-scale datasets, applying techniques such as backfilling, dimensionality reduction (e.g., PCA), feature engineering, and statistical validation

Design benchmarking frameworks and controlled experiments to evaluate model performance rigorously

Apply foundation model concepts and pre-trained architectures thoughtfully within domain-specific constraints

Ensure models are observable, versioned, and continuously evaluated in live environments

Write clean, testable, and well-documented code, participating in code reviews and structured engineering workflows

Move quickly but deliberately, prioritizing correctness, reproducibility, and operational robustness over shortcuts

You might thrive in this role if you

A Bachelor’s degree in Computer Science, Mathematics, Engineering, Statistics, or a related technical field, or equivalent practical experience building and deploying production ML systems

3+ years of professional experience in machine learning or applied AI

Strong foundations in time-series modeling, statistical methods, and deep learning

Experience working with geospatial data or spatial modeling systems

Hands-on experience handling missing data, high-dimensional datasets, or large-scale data environments

Experience contributing to ML system architecture and deploying models via structured MLOps workflows

Familiarity with cloud platforms and containerized environments, as well as constraints of on-premises deployments

Comfortable working within Python-based ML ecosystems (e.g., PyTorch, TensorFlow, scikit-learn) and modern data tooling

Evidence-driven and benchmark-oriented, preferring measurable improvements over intuition alone

Collaborative, technically curious, and comfortable operating in fast-moving but high-reliability environments

Disciplined in documentation, testing, reproducibility, and engineering rigor

Bonus

Experience with foundation models, transfer learning, or fine-tuning pre-trained architectures

Exposure to transformer-based or foundation approaches for time-series forecasting

Experience with real-time inference systems or streaming data pipelines

Familiarity with time-series databases, vector databases, or feature stores

Experience integrating LLMs or building agentic systems

Background in utilities, energy systems, or other high-reliability industrial domains

This is a full-time, permanent hybrid role (four days in-office) reporting directly to the Technology Director. The salary range for this role is $85,000–$135,000. Interested candidates are invited to submit their cover letter and resume by selecting the link: