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.

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 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