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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. Themis Intelligence values a diverse workplace and strongly encourages women, people of all races, color, creed, ancestry, ethnic origin, sexual orientation, gender identity or expression, age, religion, national origin, citizenship status, disability, marital status, family status, and those with disabilities to apply. We use AI tools to help streamline parts of our recruitment process, but every application is reviewed by a member of our team. Themis is an equal opportunity employer. We are committed to providing accommodations for persons with disabilities. If you require accommodation, we will work with you to meet your needs. While we appreciate the interest of all applicants, only those selected for an interview will be contacted.