Battery Algorithms Engineering Intern

Meta•Redmond, WA

1d•$7,313 - $12,667

About The Position

Meta Reality Labs develops hardware for AI glasses, wearables, and virtual, augmented, and mixed reality experiences. The battery team develops power sources for these devices. The Battery Algorithms Engineering Intern will develop, implement, test, and validate battery algorithms to improve runtime, reduce charge time, improve performance across temperatures, and extend battery life. The intern will also build agentic workflows to accelerate battery modeling, testing, and validation, and help solve complex engineering and technology challenges. Internships last 12 to 24 weeks, with start dates throughout the year.

Requirements

Currently pursuing a Ph.D. in engineering, mechanical engineering, materials science, chemistry, physics, or a related field.
Knowledge of Li-ion battery technology, including electrochemistry, electrical characteristics, and battery testing.
Experience with equivalent circuit modeling of Li-ion cells, including parameter estimation.
Knowledge of control and estimation theory and its application to Li-ion battery modeling.
Proficiency with scripting, data processing, and dynamic simulation in MATLAB and/or Python.
Hands-on experience using AI tools, such as LLM-based coding assistants or agents, to accelerate research or engineering work.
Interpersonal skills and experience working effectively in a dynamic, team-oriented environment.
Must obtain work authorization in the country of employment by the time of hire and maintain authorization during employment.
Intent to return to a full-time degree program after completing the internship.

Nice To Haves

Experience developing battery management algorithms, such as state-of-charge, state-of-health, or charging algorithms.
Experience developing test plans to validate battery management system algorithms.
Working knowledge of consumer-electronics battery fuel gauge algorithms, including those from common fuel gauge vendors.
Experience with embedded programming in C/C++, unit testing, and firmware integration.
Knowledge of model order reduction, efficient optimization methods, and adaptive control techniques for real-time battery management on memory-constrained ICs.
Experience applying data-driven or physics-informed AI/ML methods to battery modeling, control, and health diagnostics.
Experience designing AI agents and agentic workflows, such as tool calling, Model Context Protocol (MCP) servers, retrieval, or multi-agent orchestration.
Experience evaluating and safeguarding AI outputs, such as designing evaluations, test cases, or quality checks that catch errors before results are used.
Familiarity with cell or battery testing equipment such as Maccor, Arbin, Neware, BioLogic, or equivalent.
Prior internship or work experience in high-volume consumer electronics.

Responsibilities

Collaborate with cell, algorithm, and validation engineers to develop and validate battery models, including multi-RC equivalent circuit models, that guide battery design decisions.
Propose efficient methods to identify model parameters from experimental data and validate model accuracy.
Use battery models to estimate battery life and power delivery capability across conditions such as temperature, state of charge, and aging.
Develop and validate battery algorithms that improve charge time, cycle life, and runtime.
Analyze field battery telemetry to assess existing algorithm performance and derive representative use cases for new algorithm development.
Use electrochemistry insights to propose modeling and estimation techniques and compare their efficacy.
Design agentic workflows with human-in-the-loop review to automate repetitive battery engineering tasks, including test data processing, model parameter identification, model validation, and field telemetry triage.
Package battery models, simulations, and analysis scripts as tested, well-documented tools for reproducible use by engineers and AI agents.