Remote Deep Learning Engineer: Distributed Training Expert

BairesDev; Montes de María; A distancia; COP 374.895.000 - 562.342.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Remote work

Competitive USD salary

Hardware provided

Flexible hours

Paid parental leave

Multinational team

Growth opportunities

Descripción de la vacante

BairesDev is seeking a Deep Learning Engineer (Distributed) to lead the development and optimization of large-scale model training across multi-GPU and multi-node environments. You will focus on maximizing throughput and scalability while ensuring models train efficiently on high-performance compute clusters.

This role is 100% remote with excellent USD compensation or local currency, flexible hours, and a supportive, multinational team.

Formación

• 4+ years of experience in ML engineering or distributed systems.
• Expertise with PyTorch distributed training frameworks (DDP, FSDP, DeepSpeed).
• Familiarity with CUDA and Nvidia profiling tools (Nsight).
• Experience with performance optimization using Triton or TensorRT.
• Excellent command of English.

Responsabilidades

• Design and implement distributed training pipelines using PyTorch DDP, FSDP, and DeepSpeed.
• Optimize model performance with mixed-precision training and sharding.
• Profile and resolve bottlenecks with Nsight and related tools.
• Develop optimized kernels with Triton/TensorRT for speed.
• Collaborate with research and systems teams on distributed state and checkpoints.

Conocimientos

PyTorch DDP/DeepSpeed

Distributed systems

CUDA/Nsight

Triton/TensorRT

English proficiency

Herramientas

CUDA toolkit

Descripción del empleo

BairesDev is seeking a Deep Learning Engineer (Distributed) to lead the development and optimization of large-scale model training across multi-GPU and multi-node environments. You will focus on maximizing throughput and scalability while ensuring models train efficiently on high-performance compute clusters.

This role is 100% remote with excellent USD compensation or local currency, flexible hours, and a supportive, multinational team.