Meta
Software Engineer, Wearables Interfaces Engineering
$184K–$257K
Redmond, WA +1 · Full-time · Posted Jun 8, 2026
- Location
- Redmond, WA
New York, NY - Experience
- 10+ years of experience in systems engineering, platform software, or embedded systems development.
- Education
- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a relevant technical field, or equivalent practical experience.
About the job
Leads platform software architecture and integration for Meta wearable devices, connecting Core OS capabilities with applications and optimizing system performance, reliability, and power efficiency.
What you'll do
- Drive architecture and integration across Core OS interfaces, frameworks, middleware, and applications; lead cross-layer system initiatives.
- Debug complex platform issues and optimize performance, latency, memory use, and power consumption across the stack.
- Partner with Core OS and cross-functional teams to define requirements, establish reliability metrics, and align technical direction.
What they ask for
- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a relevant technical field, or equivalent practical experience.
- 10+ years of experience in systems engineering, platform software, or embedded systems development.
- Experience leading complex platform integration across OS, frameworks, and application teams, with demonstrated organization-level impact.
- Expertise in platform architecture and proficiency in C/C++ and/or Java/Kotlin, including debugging across multiple stack layers.
Nice to have
- Experience shipping consumer electronics or embedded devices at scale; experience with Android platform internals.
- Experience with wearable devices, AR/VR, mobile platforms, sensor systems, connectivity, multimedia, or neural interfaces.
Pay
$183,997/year to $257,000/year USD (base salary); bonus and equity amounts not stated
Tools
C/C++, Java/Kotlin, Linux, Android, Python, Shell
AI focus
Use AI tools and workflows to accelerate development, analysis, and engineering efficiency; maintain AI skills and apply responsible AI practices.
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