Machine Learning Engineer, Energy Hardware Engineering

Role responsibilities

Develop machine learning and statistical methods to enhance understanding of fleet behavior and improve system models. Establish a unified fleet data layer and consistent AI/ML standards for systems engineering teams.

Requirements

Candidates should have a strong quantitative foundation in physics or applied math, along with solid machine learning fundamentals. Experience with scientific ML and large-scale data handling is also required.

Key skills

Machine Learning, Statistical Methods, Data Modeling, Uncertainty Quantification, Probabilistic Modeling, Time-Series Analysis, Python, SQL, Spark, Version Control, CI/CD, Containerization, Collaboration, First Principles Reasoning, Physics-Informed ML, Data Ingestion

Keywords

Machine Learning, Statistical Methods, Data Modeling, Uncertainty Quantification, Probabilistic Modeling, Time-Series Analysis, Python, NumPy, Pandas, PyTorch, JAX, SQL, Spark, Version Control, CI/CD, Docker, Kubernetes, Physics-Informed ML, Surrogate Modeling, Differentiable Simulation, System Identification, Data Ingestion, Fleet Data, AI Standards, Collaboration, First Principles, Energy Products, Tesla, Field Quality, Risk Assessment