Machine Learning Engineer, Energy, Service

Tesla Inc

  • Palo Alto, CA
  • 30+ days ago
  • $124,000–$210,000 Per Year

Highlights

Teslas Energy Intelligence team needs a hands-on technical expert who can architect and build production ML systems that transform massive streams of energy telemetry into predictive intelligence, keeping our industrial, residential, Supercharger, Solar, and future energy products running at peak health. Architect and deploy advanced ML systems for real-time anomaly detection, fault prediction, root cause analysis, and performance optimization across diverse energy telemetry streams (voltage, current, temperature, power output, efficiency metrics, etc.).

Numbers & Facts

LocationPalo Alto, CA
Salary$124,000–$210,000 Per Year

Description

Teslas Energy Intelligence team needs a hands-on technical expert who can architect and build production ML systems that transform massive streams of energy telemetry into predictive intelligence, keeping our industrial, residential, Supercharger, Solar, and future energy products running at peak health.

You will merge deep understanding of energy systems with cutting-edge ML/AI techniques to detect anomalies, predict failures, and enable autonomous diagnostics across our global energy fleet.

You will build production-grade anomaly detection frameworks, predictive maintenance systems, and agentic AI workflows that isolate performance degradation, forecast component failures, and trigger automated mitigation strategies across Industrial Energy, Residential Energy, and Solar Product & Service Engineering.

Your work will eliminate downtime, prevent catastrophic failures, and fundamentally transform how Tesla diagnoses and resolves fleet-wide issues before they impact customers.

Architect and deploy advanced ML systems for real-time anomaly detection, fault prediction, root cause analysis, and performance optimization across diverse energy telemetry streams (voltage, current, temperature, power output, efficiency metrics, etc.).

Design physics-informed ML frameworks that combine domain knowledge from electrical and mechanical engineering with state-of-the-art data-driven techniques for superior model performance and generalization.

Build and scale production ML pipelines that process high-volume, multi-modal time-series data from millions of energy assets with sub-second latency and high reliability.

Develop interpretable AI systems that explain anomalies, predictions, and recommended actions to technical and non-technical stakeholders, enabling confident decision-making.

Create agentic AI workflows that autonomously detect, diagnose, prioritize, and recommend remediation for operational and maintenance challenges across global energy fleets.

Partner with data engineering, product, firmware, and service teams to define telemetry requirements, feature engineering strategies, model evaluation frameworks, and deployment architectures.

Requirements:

  • 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 a strong focus on time-series analysis, anomaly detection, or predictive maintenance.
  • Deep expertise in ML frameworks and tools: PyTorch, TensorFlow, scikit-learn, and specialized time-series libraries (Prophet, NeuralProphet, GluonTS, tslearn, Kats).
  • Strong programming skills in Python with proficiency in at least one compiled language (C, Rust, Go) for performance-critical components.
  • Proven experience working with large-scale telemetry datasets, streaming data pipelines, and real-time inference systems.
  • Deep understanding of statistical methods for anomaly detection, forecasting, change point detection, and causal inference.
  • Strong technical communication skills - ability to explain complex ML concepts and collaborate effectively with cross-functional teams.
  • Background in energy systems - power electronics, battery management systems, inverter control, thermal dynamics, or grid operations - with the ability to encode domain physics into ML models.
  • Experience with edge ML and model optimization - quantization, pruning, and deployment on resource-constrained embedded systems.
  • Open-source contributions to ML frameworks, time-series libraries, or energy analytics tools.

Benefits:

Along with competitive pay as a full-time Tesla employee, you are eligible for the following benefits at day 1 of hire:

  • Medical plans: Plan options with 0 payroll deduction.
  • Family-building: Fertility, adoption, and surrogacy benefits.
  • Dental: Including orthodontic coverage.
  • Vision plans: Both have options with a 0 paycheck contribution.
  • 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).
  • 401k with employer match.
  • Employee Stock Purchase Plans and other financial benefits.
  • 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 to include critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance.
  • Weight Loss and Tobacco Cessation Programs.
  • Tesla Babies program.
  • Commuter benefits.
  • Employee discounts and perks program.

Expected Compensation:

$124,000 - $210,000 annual salary, cash, and stock awards.

Pay offered may vary depending on multiple individualized factors including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

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