The Device & Embedded group within Applied AI focuses on enhancing artificial intelligence models that support low-level system development, including bootloaders, operating systems, kernels, drivers, and intermediate system services. We are seeking an embedded systems engineer to convert deep domain expertise into high-quality training signals for Meta's frontier coding models. In this role, you will analyze complex low-level engineering challenges from this domain, construct rigorous evaluations and trajectory data for model training, and identify areas requiring model improvement. While the position involves direct, hands-on engineering, the primary deliverable is an optimized model rather than a standard product feature. This fast-paced role is ideal for engineers who wish to leverage their systems-level expertise to transform software engineering methodologies.
Responsibilities
Collaborate with cross-functional teams (product, design, operations, infrastructure) to help Meta's AI model build platforms for Android, Linux & RTOSes (Zephyr, FreeRTOS)
• Analyze and optimize code for quality, efficiency, and performance, and provide feedback to peers during code reviews
• Set direction and goals for teams, lead major initiatives, provide technical guidance and mentorship to peers, and help onboard new team members
• Architect efficient and scalable systems that drive complex applications
• Identify and resolve performance and scalability issues, and drive large efforts to reduce technical debt
• Work on a variety of coding languages and technologies
• Establish ownership of components, features, or systems with expert end-to-end understanding
Minimum Qualifications
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 8+ years of experience in embedded software engineering, including development in C or C++ for resource-constrained systems
• Experience developing and debugging software across multiple embedded platforms, including RTOS environments and Linux or AOSP on application processors
• Experience writing device drivers or hardware abstraction layers for peripherals such as sensors, power management ICs, displays, or communication buses (I2C, SPI, UART, USB)
• Experience building telemetry, logging, or monitoring infrastructure to track embedded system health and diagnose production issues at scale
• Experience developing automated test infrastructure for embedded systems, including hardware-in-the-loop testing, on-device automation, or CI pipelines targeting embedded targets
• Experience debugging complex cross-layer embedded issues using tools such as JTAG debuggers, logic analyzers, oscilloscopes, or static analysis tools
Preferred Qualifications
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience working with AI coding assistants and evaluating their output for correctness, safety, and adherence to embedded development standards
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• kernel internals (Android or Linux or RTOS), plus device driver development across common subsystems
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience collaborating with silicon or chipset vendors on firmware bring-up, reference design adaptation, and hardware errata mitigation
• Experience in leveraging AI tools to accelerate embedded development workflows, automate diagnostics, or improve code quality and test coverage
