About the role#
We are looking for an intern to design and train neural networks that run efficiently on custom hardware for wearable and robotics devices. You will co-design models and systems that operate under tight accuracy, latency, power, and memory budgets. This role involves owning your work from training through validation on real hardware, including FPGA prototypes.
What you'll do#
- Design, train, and evaluate neural networks for on-device workloads under compute, memory, and energy constraints.
- Develop hardware-aware training and model compression techniques and measure their impact on target hardware.
- Explore partitioning models across multiple accelerators or chips, including how inter-chip communication affects accuracy and performance.
- Profile models end to end and use measurements to drive model and hardware design decisions.
- Prototype and validate on FPGA platforms, modifying and debugging existing RTL as needed.
- Present results through a working demo and a technical write-up.
What you'll need#
- Currently pursuing a PhD in Computer Science, Electrical/Computer Engineering, Machine Learning, or a related field.
- Hands-on experience training deep neural networks in PyTorch or TensorFlow, from data pipeline through debugging and evaluation.
- Working knowledge of computer architecture, including how memory hierarchy, data movement, and numerical precision affect neural network performance.
- Intent to return to full-time degree program after completion of the internship.
- Ability to obtain and maintain work authorization in the United States.
- Preferred: RTL design in Verilog or SystemVerilog, including simulation and testbenches.
- Preferred: FPGA experience, including synthesis, timing closure, and on-board bring-up and debug.
- Preferred: Experience with quantization-aware training, mixed precision, pruning, or knowledge distillation.
- Preferred: Experience with neural network accelerators, model parallelism, or distributed inference.
- Preferred: C/C++ and model deployment on embedded or edge platforms.
- Preferred: Publications or substantial projects in efficient ML, ML systems, or computer architecture, such as MLSys, NeurIPS, ICLR, ISCA, MICRO, or DAC.
Location & details#
- Location: Redmond, Washington
- Modality: On-site
- Employment: Full-time internship
- Compensation: $7,313 to $12,667 per month
About Meta
Meta develops software and social media applications. It operates platforms such as Facebook, Messenger, Instagram, and WhatsApp. The company maintains headquarters in Menlo Park, California. Current work focuses on augmented and virtual reality alongside artificial intelligence and machine learning.
How to get in at Meta
Landing an internship at Meta requires a proactive approach to the application process. Intern Insider sends an instant alert the moment a role matching your target is published, allowing you to apply among the first. Early applicants often have their materials reviewed before the applicant pool grows significantly. You can use Intern Insider to surface the recruiters behind specific roles at Meta. Reaching out directly to these individuals to ask about the position or a referral often improves your response rates compared to applying into a queue.
