Machine Learning Digital Design Engineer
Meta· Social Media· Jul 14, 2026 · 4mo agoPosted 105 days ago
Sunnyvale, CA
In office
Full-time
Staff
$180K–$242K est.
8+ years exp
Before you apply
Ghost Signal:
Open since July 14, 2026 — 105 days, and still accepting applications.
Job description
Meta is seeking highly skilled Design Engineers to join our team. 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.
Contribute to ASIC digital µArchitecture and design Assist 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 IP/sub-system micro-architecture and RTL design in collaboration with DV and PD leads
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 8+ years of experience as a Hardware Design Engineer for production silicon shipped in volume Experience in digital design µArchitecture, 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 Experience in ML accelerator subsystems and top level design Experience in SoC integration and ASIC architecture Knowledge of microcontrollers, DSP, CDC and power sequence 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
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Keywords to mirror
machine learningmlai
- Platform
- meta
- Effort
- medium