Battery Modeling Engineer

Base is America’s next-generation power company. We’re rebuilding the foundation of modern civilization–electricity–by deploying a vast network of distributed batteries that is transforming today’s fragile, centralized grid into a resilient and abundant system. We are engineers, operators, and creatives solving some of the most complex, interdisciplinary challenges of our time.

As a Battery Modeling Engineer, you’ll build the data and degradation models that tell us how our batteries age in the real world. You’ll combine software engineering, data science, and semi-empirical degradation modeling, turning telemetry from our fleet of deployed home batteries and data from cell-level testing into trusted datasets and models that predict capacity fade, resistance growth, and remaining useful life across the conditions our batteries actually see.

This is a hybrid role at the intersection of software, data science, and battery science. You’ll own degradation analytics end to end, from the pipelines and data models that make fleet and lab data trustworthy to the semi-empirical models and tools the rest of Base relies on, and you’ll help found and set up Base’s cell characterization lab. Your work will directly shape how we operate our fleet and plan for its long-term performance.

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  Build and validate semi-empirical degradation models (calendar and cycle aging, capacity fade, resistance growth) for the cells in our fleet.

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  Help found and set up Base’s cell characterization lab: define test plans, select equipment, and stand up the processes that generate reliable aging data.

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  Analyze fleet- and device-level data to understand how temperature, state of charge, and usage patterns drive degradation, and validate model predictions against real-world behavior.

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  Turn model outputs into tools and insights that engineering, fleet, and markets teams use to decide how our batteries are operated.

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  Write production-quality Python and SQL (or equivalent), with the tests, documentation, and monitoring that keep models trustworthy as the fleet grows and ages.

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  Track model history and communicate those changes effectively across the organization.

• A background in Chemistry or Battery Physics or a related field such as electrochemistry, materials science, or chemical engineering.

• PhD in physics modeling or battery chemistry preferred.

• A world-class ownership mindset: you own outcomes, not just tasks.

• Hands-on experience with battery modeling, ideally semi-empirical or physics-informed degradation models for lithium-ion cells.

• Strong software and data skills: proficiency in Python and SQL (or equivalent).

• Familiarity with cell testing and characterization (cycling, HPPC, EIS, or similar), and excitement about building a lab from the ground up.

• Nice to have: experience with field or fleet data from deployed energy storage, EVs, or consumer electronics.

Base’s Modeling team owns how we understand the health and performance of our battery fleet. Every battery we deploy is a long-lived asset, and how it ages determines how we operate it, how it supports the grid, and how much value it creates for our members over its life. As the fleet grows across more homes, climates, and operating conditions, that question gets harder and more valuable to answer. This team gets to build Base’s degradation modeling capability from the ground up, from a new cell characterization lab to models running on fleet-scale telemetry, so we can predict how our batteries will age and make better decisions about every battery in the field.

Please note: Base is a startup, which means priorities shift and evolve quickly. Your role may expand or change based on the needs of the business at any given time, so the responsibilities listed may not be exhaustive.

• First Principles Thinking: Question assumptions. Principles > rules.

• Operate at Base Pace: Focus on what matters, act quickly, and learn by doing.

• Give & Get Feedback: Be direct, be humble, and maintain a growth mindset.

• Everyone’s an Owner: Follow through on commitments and own results.

• Strong Opinions, Loosely Held: Drive clarity and make calls with imperfect information.

• Committed to the Mission: Rebuilding the grid is a big challenge. We work hard because we care deeply about the impact we’re creating. We work in-person. It’s not a 9-to-5. We are all-in.

• Fun & Optimism Coexist with Grit: Collaboration and celebration coincide with the intensity of building real things.