Battery Algorithm Engineer

Meta; Sunnyvale, Redmond (CA, WA); On-site; USD 180,000 - 240,000; Full time

Job summary

Meta Reality Labs seeks a Battery Algorithm Engineer to define and lead the development of advanced battery management algorithms and state estimation systems for VR/AR devices. You will own the long-term technical strategy for battery algorithms across Reality Labs hardware, driving innovations in SOC, SOH, cell balancing, and adaptive charging.

You will collaborate across hardware, firmware, and electrochemistry teams, applying data-driven AI/ML approaches and interfacing with BMS ICs and RTOS

Qualifications

• 8+ years of experience developing battery management system algorithms.
• Experience with electrochemical modeling techniques for Li-ion chemistries.
• Experience leading cross-functional programs across hardware, firmware, and electrochemistry teams.
• PhD in EE or electrochemistry with 8+ years in battery algorithms.
• Experience with data-driven AI/ML for battery control and health diagnostics.

Responsibilities

• Define and lead development of battery management algorithms.
• Own long-term technical strategy for battery algorithms across Reality Labs hardware portfolio.
• Drive improvements in state-of-charge estimation and state-of-health prediction.
• Collaborate with hardware, firmware, and electrochemistry teams from algorithm definition to product validation.
• Communicate design decisions and trade-offs to both engineering and non-engineering stakeholders.

Skills

Battery management

State estimation

Embedded firmware

Cross-functional leadership

Data-driven AI/ML

SPI/I2C protocols

RTOS familiarity

Lithium-ion chemistry

Hardware-software interfacing

SOH / aging models

Education

PhD in Electrical Engineering or Electrochemistry

Tools

Embedded firmware environments

BMS IC integration

Real-time operating systems

Job description

Meta Reality Labs is seeking a Battery Algorithm Engineer to define and lead the development of advanced battery management algorithms and state estimation systems for next-generation virtual and augmented reality devices, including headsets, wearables, and smart glasses. In this role, you will own the long-term technical strategy for battery algorithms across Meta's Reality Labs hardware portfolio, driving innovations in state-of-charge estimation, state-of-health prediction, cell balancing, and adaptive charging to maximize energy density, safety, and longevity in highly constrained wearable form factors.

• 8+ years of experience developing battery management system algorithms, including state estimation, adaptive charging, or cell balancing for consumer electronics or wearable devices
• Experience with electrochemical modeling techniques such as equivalent circuit models, physics-based models, or data-driven approaches applied to lithium-ion or emerging cell chemistries
• Experience implementing and validating battery algorithms in embedded firmware environments, including integration with battery management system ICs and real-time operating system constraints
• Experience leading cross-functional technical programs involving hardware, firmware, and electrochemistry teams from algorithm definition through product validation
• Experience communicating complex algorithm design decisions and technical trade-offs in writing to both engineering and non-engineering stakeholders
• Experience with data-driven AI/ML algorithms for battery control and health diagnostics
• Working knowledge of communication protocols such as SPI and I2C, and interfacing with microcontroller peripherals
• Background in high-volume consumer electronics or electric vehicle battery applications
• Experience developing embedded applications for microprocessors, including familiarity with real-time operating systems
• Understanding of hardware and clock-level considerations (interrupts, delays, clock gating, polling)
• PhD in electrical engineering, electrochemistry, or a relevant field, with 8+ years of experience in developing battery algorithms
• Working-level experience with Li-ion degradation mechanisms (capacity/power fade, swell) and how they inform SOH and charge-optimization algorithms