Microsoft

Compensation

Full Description

Collaborate with architecture, silicon engineering, firmware, hardware design, hardware validation, OS (operating systems), manufacturing, and customer teams to build state-of-the-art accelerator hardware solutions. Participate in architectural discussions and evaluate networking performance required to meet the demands of AI/ML workloads Analyze new interfaces and subsystems to develop integration plans, analyze power efficiency, debug integration issues, and provide recommendations. Perform NUDD (new, unique, different and difficult) technology and feature analysis and provide risk assessment and mitigations. Drive technical requirements and ensure the solution is flexible and scalable across the full (HW/FW/SW) stack. Enable platform and solution level discussions, influencing architecture of the product, and delivering to product goals across quality, reliability, and performance. Collaborate with internal, external, and open-source partners to onboard innovative technologies in a seamless manner. Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 7+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 8+ years technical engineering experience OR equivalent experience. 8+ years of experience with developing Accelerator hardware and associated components field. 8+ years of experience with hardware, firmware, management firmware, and/or software (system & application stack) interfaces across all modules in a system. 8+ years of development/ validation experience in high speed technologies like PCIe, DDR5/6, Ethernet, Data Centre elements with an understanding of low speed interfaces, SOC Emulators, JTAG debuggers. These requirements include but are not limited to the following specialized security screenings: Ability to work across multiple disciplines (hardware, firmware, software, and/or data center infrastructure) to identify risks, drive discussions, detail system tradeoffs, and assess impact. Hands on experience developing GPU, FPGA based accelerator platforms for AI/ML used cases Knowledge of high-volume silicon (SoCs, GPUs, or FPGAs), compute, storage, and/or networking design, manufacturing, and deployment. Experience with highspeed interfaces such as PCIe, DDR, and ethernet. In depth experience with operating systems (Windows and/or Linux), system firmware (BIOS, BMC), and system security (hardware and software). Knowledge in virtualization technologies Knowledge about datacenters & operations at scale. Experience managing hardware programs through the entire product lifecycle. Strong verbal and written communication and presentation skills.