Build production machine learning and agentic AI workflows for predictive intelligence across Tesla Energy products and services.

Train and deploy production machine learning models, including computer vision, NLP, and LLM-based models, to detect, understand, or forecast quality issues
Design and build data storage and ingestion systems tailored to machine learning use cases
Perform statistical analyses to evaluate occurrence rates, risk, and root cause of quality issues
Build and deploy ML infrastructure for scalable pipelines, 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
Build and maintain dashboards, data visualizations, and metrics using BI tools such as Tableau and Power BI, including in-product dashboards to support modeling implementation
Support business leaders with strategy and quarterly planning using data-driven decisions, explaining model predictions and recommended actions to technical and non-technical stakeholders
Write unit and integration tests to validate code functionality requirements
Partner with cross-functional teams (software, data engineering, product, operations) to define data requirements, model evaluation frameworks, and deployment architectures

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 emphasis on time-series analysis, anomaly detection, classification, or computer vision
Deep expertise in ML frameworks and tools: PyTorch, TensorFlow, scikit-learn; plus LLM application frameworks such as LangGraph
Strong programming skills in Python and SQL, with proficiency in at least one compiled language: C#, Go, or C++ for production backend services
Proven experience with large-scale datasets, event-driven data pipelines (RabbitMQ, Kafka), and real-time inference systems
Strong understanding of statistical methods for anomaly detection, forecasting, root cause analysis, and causal inference
Strong technical communication skills for cross-functional collaboration and clear explanation of complex ML concepts
Background in energy systems (solar, energy storage, power electronics, or grid operations) to encode domain knowledge into models
Experience with model optimization and MLOps, including quantization, automated retraining, model versioning, and production monitoring

Medical plans
Family-building, fertility, adoption and surrogacy benefits
Dental (including orthodontic coverage) and vision plans
Company Paid (Health Savings Accounts) HSA Contribution when enrolled in the High-Deductible medical plan with HSA
Healthcare and Dependent Care Flexible Spending Accounts (FSA)
401(k) with employer match
Employee Stock Purchase Plans
Company paid Basic Life, AD&D
Short-term and long-term disability insurance (90 day waiting period)
Employee Assistance Program
Sick and Vacation time (Flex time for salary positions, Accrued hours for Hourly positions), and Paid Holidays
Back-up childcare and parenting support resources
Voluntary benefits: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance
Weight Loss and Tobacco Cessation Programs
Teslas Babies program
Commuter benefits
Employee discounts and perks program
Earned Wage Access: real-time pay access paid daily, subject to eligibility and schedule (applicable for below Sr. Managers levels)

USD 124,000 - 396,000 per year (annual salary) + cash and stock awards + benefits
Pay may vary based on individualized factors including market location, job-related knowledge, skills, and experience

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

Job Description