Job Description

Reality Labs focuses on delivering Meta's roadmap through creating advanced AI enabled wearables. The compute performance and power efficiency requirements of wearables require custom silicon. Reality Labs Silicon team is driving the state-of-the-art forward with breakthrough work in AI, augmented reality, computer vision, machine learning, graphics, displays, and sensors. Our chips will enable wearable devices where our real and virtual world will mix and match throughout the day.

We are growing our team and are seeking engineers who will work with a group of individuals using your skills to implement and contribute to the development and optimization of accelerators for emerging technologies and applications.

Responsibilities
Identify and solve multi-discipline ML acceleration problems involving algorithms, network design, hardware architecture and wearable use cases
* Work across hardware and software, to solve co-design problems with other research scientists working in this area
* Assist with performance/power analysis of machine learning models
* Algorithmic modeling of machine learning workloads
* Develop tooling and methodologies for efficient design of ML accelerators

Qualifications
Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
* PhD in Electrical Engineering or Computer Science or equivalent work experience
* Experience in digital design, microarchitecture, or performance modelling
* Experience with computer architecture, including CPU and CV/ML accelerators
* Experience with Machine learning models, algorithms or accelerator architecture
* Experience with at least 1 procedural programming language (C, C++, Python etc) Experience in ASIC design flow (Design, Verification, Synthesis)
* Experience with TensorFlow / Pytorch or similar machine learning toolsets
* Familiarity with the state-of-art-ML algorithm optimizations like Neural Architecture Search, quantization, pruning etc
* 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