System Engineer Intern - Efficient On-Device ML Computing
Description For System Engineer Intern - Efficient On-Device ML Computing
Meta Reality Labs (RL) is seeking a System Engineer Intern to join their pioneering team in AR/VR technology development. This role focuses on enhancing the power and performance of on-device ML execution through custom ML accelerators and innovative system architectures.
The internship, lasting 12-16 weeks, offers a unique opportunity to work with the Wearable System Architecture team, where you'll be involved in end-to-end use case analysis and optimization. You'll work on everything from UI to software/firmware frameworks, ML models, and underlying hardware blocks.
As an intern, you'll contribute to developing and optimizing ML models for edge devices, working with cutting-edge ML accelerators and parallel computing systems. You'll have the chance to perform detailed profiling and analysis, helping to shape the future of wearable technology at Meta.
The ideal candidate should have a strong academic background in Computer Science or Computer Engineering with a focus on Machine Learning. You'll need proficiency in ML frameworks like PyTorch or TensorFlow, and experience with edge ML frameworks and embedded software development.
This position offers exposure to Meta's innovative AR/VR technologies and the opportunity to work alongside experienced system architects. You'll gain valuable experience in ML optimization, system architecture, and power performance analysis while contributing to next-generation wearable technology development.
Join Meta's Reality Labs team and be part of shaping the future of immersive technologies, working on projects that push the boundaries of what's possible in AR/VR experiences. This internship provides an excellent opportunity to apply your academic knowledge to real-world challenges in one of the most innovative tech companies.
Responsibilities For System Engineer Intern - Efficient On-Device ML Computing
- Perform in-depth power and performance profiling of ML models and ML benchmarks on ML accelerators
- Examine power and performance characteristics of ML accelerators
- Develop optimal mapping definition for ML models to ML accelerators
- Identify power and performance bottlenecks and optimization opportunities
- Collaborate with cross-functional teams to prototype and productize optimizations
- Conduct power and performance analysis of end-to-end AI powered use cases
- Work with system architects on next generation ML accelerators and wearable system architecture
Requirements For System Engineer Intern - Efficient On-Device ML Computing
- Currently pursuing or obtaining a Master's degree in Computer Science or Computer Engineering with ML focus
- Proficient in ML frameworks such as PyTorch or TensorFlow
- Familiarity with edge ML frameworks like TensorFlow Lite
- Experience with edge ML accelerator compiler toolchains
- Experience in embedded software development using C/C++
- Strong understanding of computer architecture
- Must obtain work authorization in country of employment
Benefits For System Engineer Intern - Efficient On-Device ML Computing
- Competitive base compensation
- Comprehensive benefits package
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