Senior Cell Modeling & Analytics Engineer

Job Title:

Senior Cell Modeling & Analytics Engineer

Broomfield, CO or Burlingame, CA

Cell Engineering

Cell Applications Engineering Manager

Full-Time

Peak Energy is the first American venture to advance globally proven Sodium-Ion battery systems as the storage standard for the new era of renewable energy on a resilient grid.

Sodium-Ion is cheap, readily available and safe, making it the leading contender in a rapidly evolving market.

Our leadership team is powered by decades of expertise in scaling gigawatt-level innovation at world-class companies such as Tesla, Northvolt, Apple, Powin, Enovix, Zipline, Solid Power, and Fluence. We are backed by strategic investment and product partners such as TDK Ventures and Eclipse Ventures.

We are a team of engineers, operators, entrepreneurs, and partners driven by a shared ambition to set the new performance standard for renewable energy and grid resilience.

If you want to define how a new chemistry is tested, validated, and trusted at commercial scale — and to build the lab, fixtures, and methods that make that possible — we encourage you to apply and help us power a more sustainable future.

Peak Energy is seeking a Senior Cell Modeling & Analytics Engineer to develop the predictive models and data analytics that drive cell design, performance optimization, and life prediction for sodium-ion cells. The ideal candidate brings a deep understanding of battery failure mechanisms and applies that expertise across thermal and heat-generation modeling, equivalent circuit modeling, physics-based electrochemical modeling, and 3D battery modeling.

In this role you will build models that predict battery life, characterize thermal behavior, and optimize performance, and you will analyze large sets of cell performance data to surface trends, anomalies, and optimization opportunities.

The successful candidat e combines strong modeling and simulation skills with rigorous data analysis to create models that drive innovation and efficiency across our battery-powered products.

• Develop,validate, and refine battery life models to predict cell life and performance under various operating conditions.
• Develop thermal and heat-generation models at the cell level to characterize temperature distribution, heat rejection, and thermal behavior under operating and abuse conditions, and to inform cell and system thermal design.
• Build and parameterize equivalent circuit models (ECMs) for cell performance prediction, state estimation (SOC/SOH), and integration into system-level and BMS simulations.
• Develop physics-based models (e.g., electrochemical / pseudo-2D / Doyle-Fuller-Newman) to capture the underlying transport, kinetics, and degradation mechanisms that govern cell behavior and guide design decisions.
• Build 3D battery models to simulate spatially resolved electrochemical, thermal, and mechanical behavior across cell geometries and formats.
• Analyze large sets of battery performance data toidentifytrends, anomalies, and optimization opportunities and assess model accuracy.
• Collaborate with cross-functional teams, including cell engineers and ESS engineers, to integrate battery data and models into broader product-level modelsand enhance battery performance in the ESS product.
• Build and implement algorithms for advanced data analysis, using tools like Python.
• Create andmaintaincomprehensive reports, visualizations, and dashboards to communicate findings and recommendations.
• Assistinroot-cause analysisonbattery failures and contribute to improving product design and performance.
• Continuously research advancements in battery technologies, modeling methods, and data science methodologies to improve current processes.

• Bachelor's or Master's degree in Engineering, Chemistry, Materials Science, Data Science, Computer Science, ora related field.
• Minimum of 5 years of hands-on experience in battery modeling and battery data analysis.
• Deep understanding of lithium-ion and/orsodium-ion battery technologies, including their failure mechanisms.
• Hands-on experience with cell modeling approaches spanning thermal / heat-generation modeling, equivalent circuit modeling, and physics-based electrochemical modeling.
• Proficiency in programming languages such as Python, with experience in data manipulation, physical modeling, statistical modeling, and machine learning.
• Experience working with large datasets, performing statistical analysis, and using data visualization tools.
• Strong problem-solving skills and the ability to interpret and analyze complex datasets.

• PhD degree in Engineering, Chemistry, Materials Science, Data Science, Computer Science, ora related field.
• Experience in the simulation of battery materials, electrodes, and cells.
• Knowledge of electrochemical models and battery simulation tools (e.g., COMSOL, GT-Auto…