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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer, Energy Hardware Engineering - **Company:** Tesla Motors - **Location:** Palo Alto, CA, United States - **Salary:** $124,000.0 - $258,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Systems Engineering, Artificial Neural Networks, Big Data, Computer Engineering, Continuous Integration, Data Sharing, Data Visualization, Python (Programming Language), Machine Learning, NumPy, SQL Databases, Pytorch, Apache Spark, Git, Data Layers, Pandas, Containerization, Kubernetes, Data Analytics, Software Version Control, Docker - **Published:** July 15, 2026 - **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/17568324?backUrl=%2Fcareer%2F17568324%2FMachine-Learning-Engineer-Energy-Hardware-Engineering-California-Palo-Alto ## About the Role * Strong quantitative foundation - physics, applied math, or a related engineering discipline - with the ability toreason aboutphysical systems from first principles * Strong ML fundamentals: model development, training, evaluation, and a real understanding of when and why models work * Experience with physics-informed or scientific ML (e.g., physics-informed neural networks, surrogate/data-driven modeling, differentiable simulation, system identification) - or a clear aptitude and excitement to work there * Strong Python in a scientific/ML setting (NumPy, pandas,PyTorch/JAX or similar; visualization tools as needed) * Experience withuncertainty quantification, probabilistic modeling&time-series analysis * Comfort working with large-scale data and the pipelines/tooling needed to make it model-ready (SQL, Spark, and similar) * Software fundamentals: version control (Git), and familiarity with CI/CD and containerization (Docker, Kubernetes) * A collaborative, first-principles mindset,and the drive to tackle fundamental, open-ended problems ## Description paid holidays, flex time, 401(k) United States, California, Palo Alto Jul 13, 2026 What to Expect The Tesla Energy Products Field Quality team is looking for a passionate and collaborative Machine Learning Engineer to bridge large-scale fleet data to system-based modeling & analytical efforts across the Tesla Energy portfolio: Industrial, Residential, Supercharger, and Solar. You will work across the systems engineering ecosystem - building the shared data and AI/ML foundations that system teams depend on. Where our first-principles models tell us how a system should behave, you will help quantify how the fleetactually behaves, and turn that signal into better models, faster investigations, and earlier detection of issues in the field. You will helpestablisha unified fleet data layer and a consistent ML standard that systems teams can build on. What You'll Do * Develop ML and statistical methods that improve how we understand fleet behavior - from augmenting first-principles system models to detecting anomalies and emerging failure trends across energy products * Help build a unified fleet data layer and ingestion foundation that systemteams canconsume * Model the gap between expected andobservedfleet behavior and turn it into actionablesignalfor systems teams * Quantify uncertainty so teams across the ecosystem can reasonaboutrisk with statistical rigor * Establish a consistent AI/ML and data standard - training, evaluation, validation, and tooling - applied cohesively across systems engineering teams * Partner across disciplines so knowledge, methods, and integrations evolve through multi-disciplinary feedback and a shared source of fleet truth ## Related Videos - [How Machine Learning is turning the Automotive Industry upside down](https://www.wearedevelopers.com/videos/61-how-machine-learning-is-turning-the-automotive-industry-upside-down) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How software is steering vehicle technology](https://www.wearedevelopers.com/magazine/515-how-software-is-steering-vehicle-technology) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)