In this role, you will contribute to the development of advanced technology solutions, including machine learning and network acceleration. You will collaborate with researchers and engineers to design, implement, and optimize low-power hardware accelerators, state-of-the-art SoCs, and custom silicon solutions that enable the next generation of innovative devices and hardware.
Responsibilities
Contribute to ASIC digital Architecture and design
Assist in the performance/power analysis of the design and help meet power and performance targets
Work with architects to map algorithms onto the hardware and specify requirements for IP and subsystems integration
Collaborate with adjacent teams such as Verification, Physical Design, and Design for Test
Develop micro-architecture, RTL coding, and design verification for complex IPs
Drive the IP/sub-system micro-architecture and RTL design in collaboration with DV and PD leads
Minimum Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience
6+ years of experience as a hardware design engineer for production silicon shipped in volume
Experience in digital design Architecture, RTL coding, and micro-architecture development
Experience communicating technical design decisions and trade-offs to cross-functional partners such as verification, physical design, and architecture teams
Preferred Qualifications
Experience with one or more infrastructure IP areas, such as clock generation, distribution, and power management
Exposure to subsystem-level implementation or integration, in any part of the flow from specification through RTL signoff
Interest in growing into an infrastructure IP and subsystem implementation role
Experience with microcontroller clusters
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
