Remote Deep Learning Architect for Scalable AI

BairesDev; Chile; A distancia; CLP 111.317.000 - 166.976.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Remote work

Competitive salary

Home office setup

Flexible hours

Parental leave

Global team

Descripción de la vacante

BairesDev is seeking a Deep Learning Engineer to design and implement advanced neural network architectures, bridging research and production-grade software to deliver scalable AI solutions.

You will use PyTorch and Python to build end-to-end pipelines, optimize training with distributed and mixed-precision approaches, and collaborate with cross-functional teams to deploy models across enterprise applications.

Formación

• 4+ years in Deep Learning Engineering or ML-focused software roles.
• Expertise in PyTorch and custom model architectures.
• Experience with distributed training and GPU optimization.

Responsabilidades

• Design, develop, and implement custom deep learning model architectures using PyTorch and Python.
• Optimize training processes with distributed training and mixed-precision techniques for large datasets.
• Improve model performance with GPU acceleration and memory-efficient methods.
• Build end-to-end deep learning pipelines from data preprocessing to deployment.
• Collaborate with cross-functional teams to integrate AI models into enterprise applications.
• Monitor and refine production models for accuracy, stability, and scalability.

Conocimientos

PyTorch

Python

Deep learning

Distributed training

GPU optimization

Mixed-precision training

English proficiency

Large-scale training pipelines

Herramientas

CUDA

Git

Descripción del empleo

BairesDev is seeking a Deep Learning Engineer to design and implement advanced neural network architectures, bridging research and production-grade software to deliver scalable AI solutions.

You will use PyTorch and Python to build end-to-end pipelines, optimize training with distributed and mixed-precision approaches, and collaborate with cross-functional teams to deploy models across enterprise applications.