Battery Degradation Modeling Engineer (Fleet & Lab)

Hardware FYI; Austin (TX); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Base is America’s next-generation power company. We are building a distributed battery network to transform the grid and create value for our members.

As a Battery Modeling Engineer, you will turn telemetry and lab data into trusted degradation models predicting capacity fade, resistance growth, and remaining useful life across real-world conditions. This hybrid role blends software, data science, and battery science.

Qualifications

• Background in Chemistry or Battery Physics or related field.
• PhD in physics modeling or battery chemistry preferred.
• Ownership mindset: own outcomes, not just tasks.
• Hands-on experience with battery modeling, semi-empirical or physics-informed for Li-ion cells.
• Strong Python and SQL skills (or equivalent).
• Familiarity with cell testing/characterization and lab setup.
• Nice to have field data from deployed energy storage or EVs.

Responsibilities

• Build and validate semi-empirical degradation models (calendar and cycle aging, capacity fade, resistance growth).
• Help found and set up Base’s cell characterization lab: plan tests, select equipment, establish aging data processes.
• Analyze fleet and device data to link temperature, SOC, usage to degradation; validate model predictions.
• Turn model outputs into tools and insights for engineering, fleet, and markets teams.
• Develop production-quality Python and SQL with tests, docs, and monitoring.
• Track model history and communicate changes across the organization.

Skills

Battery modeling

Python

SQL

Ownership mindset

Data analysis

Education

PhD in physics or battery chemistry

Tools

HPPC testing

EIS experience

Job description

Base is America’s next-generation power company. We are building a distributed battery network to transform the grid and create value for our members.

As a Battery Modeling Engineer, you will turn telemetry and lab data into trusted degradation models predicting capacity fade, resistance growth, and remaining useful life across real-world conditions. This hybrid role blends software, data science, and battery science.