Remote Deep Learning Engineer (PyTorch)

BairesDev; Montes de María; Remote; COP 291,290,000 - 485,484,000; Full time

Benefits offered by this job

Remote work

Competitive compensation

Home setup provided

Flexible hours

Parental leave

Mentorship & growth

Job summary

BairesDev is seeking a Deep Learning Engineer with 4+ years of experience to design, train, and deploy scalable models for enterprise applications. This is a 100% remote role with competitive compensation and flexible scheduling.

You'll leverage PyTorch, distributed training, and GPU-accelerated techniques to optimize performance on large datasets, build end-to-end pipelines, and collaborate with cross-functional teams across the globe.

Qualifications

• 4+ years of experience in Deep Learning Engineering or ML-focused software engineering.
• Proven expertise in PyTorch and designing custom model architectures.
• Hands-on experience with distributed training and GPU optimization.
• Proficiency in mixed-precision training to improve memory and speed.
• Strong Python background and experience building large-scale training pipelines.
• Advanced English proficiency.

Responsibilities

• Design, develop, and implement custom deep learning model architectures using PyTorch and Python.
• Optimize training processes through distributed training and mixed-precision techniques.
• Enhance model performance with GPU acceleration and memory-efficient training strategies.
• Build end-to-end deep learning pipelines from data preprocessing to deployment.
• Collaborate with cross-functional teams to integrate AI models into enterprise apps.
• Monitor production models for accuracy, stability, and efficiency at scale.

Skills

Deep Learning

PyTorch

Distributed Training

GPU Optimization

Python

English

Tools

CUDA

Job description

BairesDev is seeking a Deep Learning Engineer with 4+ years of experience to design, train, and deploy scalable models for enterprise applications. This is a 100% remote role with competitive compensation and flexible scheduling.

You'll leverage PyTorch, distributed training, and GPU-accelerated techniques to optimize performance on large datasets, build end-to-end pipelines, and collaborate with cross-functional teams across the globe.