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# Sr. Machine Learning Engineer, Energy Optimization

- **Company:** Tesla Motors
- **Location:** Palo Alto, CA, United States
- **Experience:** Expert
- **Salary:** $140,000 - $312,000
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Continuous Integration, Data Systems, Software Debugging, Firmware, Python (Programming Language), Machine Learning, Regression Testing, Evaluation Pipelines, Apache Spark, Kubernetes, Information Technology, Machine Learning Operations, Docker
- **Published:** November 8, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/88821923/1

## About the Role

* Degree in Computer Science, Engineering, Statistics, or a related technical field, or equivalent experience
 * Strong proficiency in Python, with the ability to work productively in a large existing codebase
 * Experience shipping applied machine learning into production systems - training, evaluation, deployment, and ongoing operation - with time-series forecasting a strong plus
 * Experience owning production software through design, implementation, debugging, and operation, where correctness, performance, and maintainability matter
 * Experience validating model changes with offline metrics, simulation, or regression-style evaluation against real data
 * Experience with cloud and data technologies such as AWS, Spark, Airflow/Argo (or equivalent orchestration), Docker/Kubernetes, and CI/CD
 * Excellent communication skills and the ability to collaborate across algorithm, software, firmware, and product partners
 * Experience with Rust is a plus
 * Experience with energy systems, battery storage, or similar real-world control systems is a plus

## Description

Opticaster's mission is to accelerate the world's transition to sustainable energy by optimizing how batteries are charged and discharged to maximize value for customers and the grid. Our algorithms determine the best times to store and use energy, considering electricity prices, solar generation, and grid conditions. These optimizations power Tesla's energy products, including Megapacks, Powerwalls, Virtual Power Plants, and microgrids. With a global fleet of over 500,000 residential systems, our software operates at scale and needs robust, production-grade solutions. Opticaster also supports grid stability through programs such as demand response, and integrates with Autobidder to participate in electricity markets., Opticaster is a customer-facing product: the software you build directly shapes how hundreds of thousands of homes and energy sites store, use, and export energy every day. As a Machine Learning Engineer on Opticaster, you will own applied machine learning models - including time-series forecasting for load and solar, and other models that inform energy decisions in the field. You will develop models, improve evaluation and monitoring around them, and ship them to production so they scale to the full fleet.
What You'll Do

 * Develop, improve, and productionize ML components in Opticaster, including time-series forecasting (e.g. load and solar) and other models that feed battery dispatch
 * Leverage ML / SI models to improve Opticaster's energy decisions in production
 * Implement model and algorithm changes in the Opticaster codebase: production Python, tests, and partnership on safe rollout and firmware/release coordination
 * Build and maintain evaluation pipelines - offline metrics, simulation, benchmarking, and regression detection - to quantify ML impact and catch regressions before release
 * Diagnose field and simulation failures; distinguish model and data issues from interface or integration problems
 * Strengthen observability around production ML: monitoring, alerting, and tooling that explain model behavior on the fleet
 * Identify and resolve performance bottlenecks (CPU, memory, latency) in ML pipelines and constrained on-device / runtime paths
 * Partner with algorithm, software, firmware, and product engineers to integrate ML into Tesla's residential energy ecosystem