Machine Learning Engineer Intern

BrainChip is looking for a curious and technically-driven Machine Learning Intern Neuromorphic & Edge AI. This is a unique opportunity to move beyond standard GPU-heavy deep learning and dive into the world of Neuromorphic Computing.

As an intern, you will help bridge the gap between traditional Artificial Neural Networks (ANNs) and BrainChip’s Akida™ event-based processor. You’ll be working on the future of "AI at the Edge"—where power efficiency and real-time learning are the ultimate goals.

Key Responsibilities

• Model Conversion & Benchmarking
• Use BrainChip’s MetaTF™ framework to convert standard models (Keras/TensorFlow) into Spiking Neural Networks (SNNs).
• Test and compare the accuracy and power consumption of models running on Akida hardware versus traditional CPUs/GPUs.
• Help maintain and expand the Akida Model Zoo by training and validating new models for specific use cases (e.g., keyword spotting, gesture recognition).
• Data Pipeline & Quantization
• Pre-process datasets for event-based processing (e.g., ImageNet, CIFAR, or specialized sensor data).
• Assist in Quantization-Aware Training (QAT) to ensure models maintain high performance at low bit-widths (1, 2, or 4-bit).
• Experiment with "on-chip" learning scenarios where the model adapts to new data without retraining in the cloud.
• Software & Tools Support
• Write Python scripts to automate testing and performance profiling.
• Document your findings and create tutorials or "Jupyter Notebook" examples to help our developers and customers understand neuromorphic workflows.

Qualifications & Skills

• Education: Currently pursuing a degree (B.S., M.S., or PhD) in Computer Science, Data Science, Electrical Engineering, or a related technical field.
• Core ML Knowledge: Solid understanding of Neural Network architectures (CNNs are a must; RNNs/Transformers are a plus).
• Programming: Proficient in Python. Familiarity with TensorFlow/Keras, Pytorch and ONYX is highly preferred.
• Mathematics: Comfortable with the linear algebra and calculus concepts behind backpropagation and optimization.
• The "Neuromorphic" Edge: You don’t need to be an expert in Spiking Neural Networks yet, but you should have a strong interest in biologically-inspired AI and low-power hardware.

Why Intern at BrainChip?

• Real Hardware Access: You won't just be running simulations; you’ll be deploying code onto physical Akida PCIe and SoC kits.
• Mentorship: Work directly with senior ML researchers and hardware architects who are pioneers in the neuromorphic space.
• Impact: Your benchmarks and model optimizations could end up in the hands of global customers building the next generation of smart devices.

Intern Program

• Start and End appx 3 months
• Flexible start depends on University’s summer schedule (example May – June start)
• End Aug/Sept
• $28 per hour
• 25 hrs per week