Machine Learning Engineer, Energy Hardware Engineering

About the role

The Tesla Energy Products Field Quality team is seeking a collaborative Machine Learning Engineer to connect extensive fleet data with system-based modeling and analytics across Tesla's Energy offerings, including Industrial, Residential, Supercharger, and Solar. This role involves working within the systems engineering framework to create foundational data and AI/ML systems that support various teams. You will analyze fleet behavior to enhance models, expedite investigations, and improve issue detection in the field.

Key facts

What you'll do

• Create machine learning and statistical techniques to enhance our understanding of fleet behavior, including anomaly detection and identifying failure trends across energy products.
• Develop a unified fleet data layer and ingestion framework for system teams.
• Analyze discrepancies between expected and actual fleet behavior to provide actionable insights for systems teams.
• Measure uncertainty to enable teams to assess risk using statistical methods.
• Set a standard for AI/ML and data practices, including training, evaluation, validation, and tools, to be uniformly applied across systems engineering teams.
• Collaborate across disciplines to ensure knowledge and methods evolve through interdisciplinary feedback and a shared understanding of fleet data.

Requirements

• A solid quantitative background in physics, applied mathematics, or a related engineering field, with the ability to analyze physical systems from fundamental principles.
• Strong understanding of machine learning, including model development, training, evaluation, and insights into model functionality.
• Experience in physics-informed or scientific machine learning, such as physics-informed neural networks or surrogate modeling, or a strong willingness to learn in this area.
• Proficiency in Python within a scientific or machine learning context, including libraries like NumPy, pandas, and PyTorch/JAX, along with necessary visualization tools.
• Familiarity with uncertainty quantification, probabilistic modeling, and time-series analysis.
• Experience handling large datasets and the necessary pipelines/tools to prepare data for modeling (e.g., SQL, Spark).
• Knowledge of software development practices, including version control (Git) and familiarity with CI/CD and containerization technologies (Docker, Kubernetes).
• A collaborative mindset and a commitment to addressing fundamental, open-ended challenges.

Nice to have

• Experience in multi-disciplinary teamwork and integration of feedback across different fields.

Skills & tools

• Python, NumPy, pandas, PyTorch/JAX
• SQL, Spark
• Git, Docker, Kubernetes
• Statistical analysis and modeling techniques

Practical notes

Visa sponsorship is not available for this role. Tesla offers a comprehensive benefits package starting from day one of employment, including medical, dental, and vision plans, 401(k) with employer match, and various employee assistance programs.

Expected Compensation

$124,000 - $258,000 annual salary, plus cash and stock awards, and benefits.

Compensation may vary based on individual factors such as location, experience, and skills. Details regarding benefits will be provided upon receiving a job offer.