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Institute for Foundation Models

Research Engineer, Speech/Audio Machine Learning

Paris
Python TensorRT Apache TVM OpenVINO DeepSpeed Horovod Docker WebRTC Machine Learning Deep Learning AI
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Description

Research Engineer - Speech/Audio Machine Learning

Team: Engineering

Location: Paris

Commitment: Full-time

Workplace Type: onsite

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 production constraints 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

  • Compute Optimization: Optimize model compute graphs for target runtimes using frameworks such as TensorRT, Apache TVM, or OpenVINO. 
  • Model Compression: Perform quantization (INT8/FP8) and pruning to enable real-time execution on edge or cloud GPUs. 
  • Data Orchestration: Build reliable data pipelines for audio datasets, including automated denoising and synthetic data augmentation. 
  • Distributed Training: Help design efficient multi-node training scripts using frameworks such as DeepSpeed, 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. 
Institute for Foundation Models
Institute for Foundation Models

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