Deep Learning Engineer

Main mission

Designs and optimizes neural network architectures.

5 key responsibilities

• Design and implement neural network architectures.
• Optimize training: GPU distribution, mixed precision, efficiency.
• Reduce training and inference costs (distillation, quantization).
• Reproduce and adapt architectures from research.
• Maintain large-scale training infrastructure.

Key skills

PyTorch, JAX, TensorFlow, CUDA, Triton, distributed GPU training.

What's expected

Deep learning appears in nearly 30% of AI engineering job offers: it is the highest level of all skills.

Career paths

Research Engineer, AI Research Scientist, Training Platform Architect.