ML Accelerator IP Validation Engineer

Meta; Sunnyvale (CA); On-site; USD 178,000 - 250,000; Full time

Benefits offered by this job

Bonus

Equity

Comprehensive benefits

Job summary

Meta Reality Labs seeks a Machine Learning IP Validation Engineer to advance ML accelerators from pre-silicon to post-silicon platforms. You will design HW-SW integration plans, run validation campaigns, and optimize performance, power, and robustness across emulation, prototyping, and silicon environments.

You will partner with RTL, verification, emulation, firmware, and ML model teams to define test plans, analyze data, and communicate findings to stakeholders while contributing to a culture

Qualifications

• :Bachelor's degree in CS/CE or related field.
• :8+ years in consumer electronics or related field.
• :Experience validating ML accelerators or programmable compute IP.
• :Experience with SOC HW interfaces such as AXI, APB, AHB, OCP.

Responsibilities

• :Define HW-SW integration, validation plans for ML accelerator IP across emulation, prototyping, and silicon platforms.
• :Identify risks and mitigate issues across cross-layer interfaces.
• :Work with RTL design, verification, emulation, architecture, ML compiler, firmware/runtime, and system teams to bring up ML workloads.
• :Create test setups, collect data, and communicate results to stakeholders.

Skills

HW-SW integration

Validation planning

Data analysis

Cross-functional collaboration

Embedded systems

ML accelerators

Scripting for test automation

Prototyping & emulation

Education

Bachelor's degree in Computer Science or Computer Engineering

Tools

AXI

APB

AHB

OCP

Job description

Meta Reality Labs seeks a Machine Learning IP Validation Engineer to advance ML accelerators from pre-silicon to post-silicon platforms. You will design HW-SW integration plans, run validation campaigns, and optimize performance, power, and robustness across emulation, prototyping, and silicon environments.

You will partner with RTL, verification, emulation, firmware, and ML model teams to define test plans, analyze data, and communicate findings to stakeholders while contributing to a culture