Camera Hardware Integration Engineer

Meta Meta · Big Tech · Sunnyvale, CA

This role is for a Hardware Electrical Engineer focused on designing, developing, and integrating electrical systems for consumer hardware. While the role mentions integrating AI tools to optimize workflows and ongoing AI skill development, the core responsibilities and deliverables are in hardware engineering, not AI model development or deployment. The AI aspects appear to be tools used by the engineer to improve their hardware design process.

What you'd actually do

  1. Lead the electrical development of highly complex systems or multiple subsystems, driving technical direction and architecture
  2. Define and drive electrical architecture, system integration, and technical strategy for major projects
  3. Lead cross-functional design reviews, resolve technical conflicts, and ensure alignment across engineering disciplines
  4. Drive innovation by identifying and implementing new technologies, processes, or methodologies to improve system/product performance, reliability, and features
  5. Oversee hardware bring-up, debugging, and support for manufacturing, assembly, deployment, or operational readiness at scale

Skills

Required

  • Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, or a related field, and/or equivalent industry experience
  • 8+ years of experience in electrical design, including system-level architecture and integration
  • Demonstrated technical leadership in at least one electrical engineering domain (e.g., power, display, sensors, infrastructure, high-speed interfaces, analog/mixed-signal)
  • Documented experience delivering products or systems from concept through prototyping, validation, and mass production or large-scale deployment
  • Experience leading cross-functional engineering teams and mentoring engineers
  • Proficiency with industry-standard design, simulation, and analysis tools (e.g., Cadence, Altium, SPICE, MATLAB, or similar)
  • Experience communicating technical decisions to technical and non-technical stakeholders (e.g., design reviews, technical specifications, project updates)
  • Experience with HW-SW integration
  • Experience with circuit design across analog, digital, power, and communications domains

Nice to have

  • M.S. or Ph.D. in Electrical Engineering or Computer Engineering
  • 12+ years of industry experience in electrical design
  • Consumer electronics experience and/or working within a mass-production environment
  • Experience integrating complex camera subsystems into system-level hardware platforms
  • Experience with end-to-end development of camera module electrical subsystem, system integration, and validation from the concept phase to mass production
  • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  • Experience working with CM/JDM manufacturing partners, including design reviews, test bring-up, build support and failure analysis
  • Experience with high-speed interfaces, signal integrity analysis, EMI trade-offs, or thermal validation in compact, power-constrained wearable form factors
  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

What the JD emphasized

  • electrical design
  • system-level architecture and integration
  • delivering products or systems from concept through prototyping, validation, and mass production or large-scale deployment
  • integrating complex camera subsystems into system-level hardware platforms
  • end-to-end development of camera module electrical subsystem, system integration, and validation from the concept phase to mass production
  • AI tools to optimize/redesign workflows
  • drive measurable impact
  • efficiency gains
  • quality improvements
  • ongoing AI skill development
  • prompt/context engineering
  • agent orchestration
  • responsible, ethical AI practices
  • risk assessment
  • bias mitigation
  • quality and accuracy reviews