System Software / Embedded Engineer

Accountabilities:: As a System Software / Embedded Engineer, you will contribute to the design, development, validation, and optimization of platform software for advanced AI inference systems. Your responsibilities will include: Develop system-level software: Gather requirements, design, implement, and maintain low-level services, tools, and supporting tests for AI inference platform software. Build and debug embedded systems: Work on firmware, board bring-up, bootloaders, board support packages (BSPs), device drivers, and Linux kernel modules. Support hardware and silicon bring-up: Collaborate with hardware, silicon, and validation teams to bring up, debug, and validate platform software on AI inference systems. Enable hardware/software co-design: Work directly with hardware and silicon engineers to troubleshoot integration challenges and ensure software and hardware components operate reliably together. Develop debugging and profiling capabilities: Create and improve tools for system analysis, diagnostics, performance profiling, and root-cause investigation. Optimize performance and power consumption: Analyze system behavior and implement improvements to maximize inference platform efficiency, responsiveness, and reliability. Work with low-level hardware interfaces: Debug and integrate components using interfaces and protocols such as PCIe, I2C, SPI, and JTAG. Ensure production-quality delivery: Develop robust, maintainable software and comprehensive tests that support platform validation and deployment in data center environments. Collaborate across engineering disciplines: Coordinate with software, hardware, silicon, and validation teams to resolve technical issues and deliver platform milestones. Support external engineering activities: Travel occasionally to partner, customer, or contract manufacturing sites when required; travel is not mandatory. Requirements: The ideal candidate combines strong embedded software engineering expertise with hands-on hardware debugging experience and the ability to work effectively across multiple engineering disciplines. Key qualifications include: Professional experience: At least 5 years of experience developing low-level systems software, embedded software, firmware, and engineering tools for high-performance computing systems deployed to data center customers. Linux and embedded systems: Practical experience bringing up and debugging complex Linux-based systems at the kernel and driver levels, including embedded Linux, device drivers, and/or real-time operating system (RTOS) environments. Silicon and SoC experience: Experience with system-on-chip or silicon bring-up is highly desirable. Programming expertise: Advanced proficiency in C and C++, along with working proficiency in Python, Bash/Shell, or related scripting languages. Rust experience is an advantage. Hardware debugging: Comfort working directly with hardware, interpreting schematics and datasheets, and troubleshooting with tools such as JTAG debuggers, logic analyzers, and oscilloscopes. Hardware interfaces: Familiarity with low-level interfaces and protocols such as PCIe, I2C, SPI, and JTAG. AI and data center environments: Familiarity with AI inference frameworks and data center deployment or management environments is preferred. Education: A bachelor's or master's degree in Electrical Engineering, Computer Science, or a related technical discipline. Cross-functional collaboration: Ability to work effectively with hardware, silicon, validation, and software teams to solve complex integration problems. Communication skills: Excellent technical communication skills, including the ability to explain complex concepts, debugging findings, and proposed solutions clearly. Adaptability and ownership: A self-motivated, adaptable approach to problem-solving, with the ability to take ownership of technical challenges and deliver results in a fast-paced startup environment. Commitment to engineering excellence: Strong attention to quality, integrity, continuous improvement, and collaborative problem-solving. Benefits: Competitive compensation: A competitive compensation package. Comprehensive benefits: Benefits designed to support employees and their families. Flexible spending options: Flexible spending benefits to support individual needs. Employee recognition: Recognition programs that acknowledge contributions and achievements. Collaborative environment: The opportunity to work with talented engineers across software, hardware, and silicon disciplines. Innovative technical work: The opportunity to contribute to high-performance, low-power AI inference systems and emerging generative AI technologies. Flexible collaboration: A distributed working environment with team members across North America and Europe. Professional growth: Opportunities to tackle complex engineering challenges and contribute to ambitious technology development.