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# Sr. Machine Learning Engineer, Energy Core Services, Digital Experience

- **Company:** Tesla Motors
- **Location:** Fremont, CA, United States
- **Experience:** Expert
- **Salary:** $124,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Frameworks, Computer Vision, Big Data, C Sharp (Programming Language), C++ (Programming Language), Software Code Optimization, Computer Programming, Information Engineering, Data Visualization, Python (Programming Language), Machine Learning, Natural Language Processing, RabbitMQ, Power BI, Tensorflow, SQL Databases, Tableau (Software), Data Storage Technologies, Pytorch, Large Language Models, Model Validation, Backend, Build Management, Scikit Learn, Information Technology, Apache Kafka, Machine Learning Operations, Software Version Control, Data Pipelines, Golang
- **Published:** October 21, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/88691361/1

## About the Role

* Degree in Electrical Engineering, Mechanical Engineering, Physics, Computer Science, Applied Mathematics, or equivalent experience
 * 3+ years of hands-on experience building and deploying production ML models, with strong focus on time-series analysis, anomaly detection, classification, or computer vision
 * Deep expertise in ML frameworks and tools (PyTorch, TensorFlow, scikit-learn) and LLM application frameworks (e.g., LangGraph)
 * Strong programming skills in Python and SQL, with proficiency in at least one compiled language (C#, Go, C++) for production backend services
 * Proven experience working with large-scale datasets, event-driven data pipelines (e.g., RabbitMQ, Kafka), and real-time inference systems
 * Deep understanding of statistical methods for anomaly detection, forecasting, root cause analysis, and causal inference
 * Strong technical communication skills - ability to explain complex ML concepts and collaborate effectively with cross-functional teams
 * Background in energy systems - solar, energy storage, power electronics, or grid operations - with ability to encode domain knowledge into ML models
 * Experience with model optimization and MLOps - quantization, automated retraining, model versioning, and production monitoring

## Description

You will build production-grade machine learning models and agentic SI workflows that detect issues, assess risk, forecast outcomes, and trigger automated actions across Residential Energy, Commercial Energy, Charging, and Solar Product & Service Engineering. Your work will reduce delays and rework, prevent failures, and transform how Tesla identifies and resolves issues before they affect customers.
What You'll Do

 * Train and deploy various types of machine learning models to production, including computer vision, NLP, and LLM-based models, that assist in understanding, detecting, or forecasting quality issues across Tesla Energy products and services
 * Design and build data storage and ingestion systems specifically for machine learning use cases
 * Complete statistical analyses to assist in understanding the occurrence rate, risk, and root cause of quality issues
 * Build and deploy infrastructure that supports scalable machine learning processes, including automated retraining, model versioning and evaluation, and low-latency real-time inference
 * Create agentic SI workflows that autonomously detect, diagnose, prioritize, and recommend remediation for operational challenges across Tesla Energy products and services
 * Build and maintain dashboards, data visualizations, and metrics using BI tools such as Tableau and Power BI, as well as in-product dashboards, to support modeling use cases and implementation
 * Assist business leaders in developing their strategy and quarterly planning through data-driven decisions, explaining model predictions and recommended actions to technical and non-technical stakeholders
 * Write unit and integration tests to ensure that code functionality requirements are met
 * Drive systems improvement by partnering with cross-functional software, data engineering, product, and operations teams to define data requirements, model evaluation frameworks, and deployment architectures