System Engineer Intern - Efficient On-Device ML Computing

Requirements

Currently has, or is in the process of obtaining a Master’s degree in Computer Science, or Computer Engineering with a focus on ML.
Proficient in ML frameworks such as PyTorch or TensorFlow.
Familiarity with edge ML frameworks like TensorFlow Lite or similar technologies.
Experienced with edge ML accelerator compiler toolchains, including ARM Vela or others.
Experience in embedded software development using C/C++.
Strong understanding of computer architecture.
Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment.
Currently holds or is pursuing a PhD in Computer Science or Computer Engineering with a focus on ML.
Experience in developing and optimizing ML models for edge devices.
Familiarity with ML accelerators and their internal architecture.
Knowledge of ML model optimization techniques, such as quantization and pruning.
Experience with profiling ML model execution on and off-device.
Familiarity with power optimization techniques, such as DVFS, power and clock gating.
Intent to return to degree-program after the completion of the internship.

Responsibilities

Perform in-depth power and performance profiling of ML models and ML benchmarks on ML accelerators.
Examine the power and performance characteristics of ML accelerators in relation to various types of ML models.
Develop an optimal mapping definition for ML models to ML accelerators.
Identify power and performance bottlenecks and optimization opportunities in ML models, ML accelerators, and system architectures.
Collaborate with cross-functional teams to prototype and productize optimizations.
Conduct power and performance analysis of end-to-end AI powered use cases, identify power optimization opportunities in software, firmware and overall system architecture.
Work alongside system architects to create a roadmap for the next generation of ML accelerators and wearable system architecture.

FAQs

What is the duration of the System Engineer Intern position?

The internship lasts between twelve (12) to sixteen (16) weeks.

What educational qualifications are required for this internship?

Candidates should be currently pursuing or have obtained a Master's degree in Computer Science or Computer Engineering with a focus on Machine Learning (ML).

Are PhD candidates considered for this internship?

Yes, candidates who hold or are pursuing a PhD in Computer Science or Computer Engineering with a focus on ML are preferred.

What technical skills are necessary for the role?

Candidates should be proficient in ML frameworks such as PyTorch or TensorFlow, familiar with edge ML frameworks like TensorFlow Lite, and have experience in embedded software development using C/C++.

Is prior experience with ML accelerators required?

While not required, familiarity with ML accelerators and their internal architecture is preferred, along with experience in developing and optimizing ML models for edge devices.

What are the responsibilities of the intern in this position?

Responsibilities include performing power and performance profiling of ML models, developing optimal mapping definitions for ML models, identifying bottlenecks, and collaborating with cross-functional teams to prototype optimizations.

Is work authorization required for this role?

Yes, candidates must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.

What kind of optimization techniques should candidates be familiar with?

Candidates should have knowledge of ML model optimization techniques, such as quantization and pruning, and familiarity with power optimization techniques like DVFS and power gating.

Does Meta provide accommodations for candidates with disabilities?

Yes, Meta is committed to providing reasonable accommodations for candidates with disabilities in their recruiting process.

What are the benefits offered by Meta for this internship?

Meta offers various benefits in addition to base compensation; details can be learned through their benefits information.