Research Engineer - Speech/Audio Machine Learning

About the Institute of Foundation Models

We are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy.

As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers.

The Role

As a Research Engineer specializing in speech/audio machine learning, you will bridge the gap between research and productionconstraints to deliver competitive performance. You will be responsible for the end-to-end performance of our machine learning speech systems, from architecting scalable training infrastructure to achieving extreme low-latency inference.

Key responsibilities

• ComputeOptimization :Optimizemodel compute graphs for target runtimes using frameworks such asTensorRT,Apache TVM, orOpenVINO.
• Model Compression :Performquantization(INT8/FP8)andpruning to enable real-time execution on edge or cloud GPUs.
• Data Orchestration : Buildreliabledatapipelines for audio datasets, including automated denoising and synthetic data augmentation.
• Distributed Training :Helpdesignefficientmulti-nodetraining scripts using frameworks such asDeepSpeed,Horovod, as well as other distributed training technologies.
• Deployment: Build and maintain high-performance demo environments on the cloud using Docker and standalone model serving frameworks. Integrate WebRTC or similar bidirectional protocols to ensure seamless, real-time audio-to-audio interactions.

Qualifications

• MSc or BSc in Computer Science or Software Engineering.
• Engineering Excellence : Strong background in Systems Programming and Computer Architecture.