Software Engineer, Embedded Systems

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

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