Senior Distributed Training Engineer — Remote & Flexible Hours

BairesDev; Peru (IL); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

100% remote work

Competitive USD compensation

Hardware provided

Flexible hours

Paid parental leave and holidays

Job summary

BairesDev is hiring a Distributed Training Engineer to own scaling and optimization of training workloads across multi-GPU and multi-node environments. You will ensure peak performance for massive datasets and models with mixed-precision and distributed coordination.

You will collaborate with ML research teams and design infrastructure to support evolving model scaling requirements while enjoying 100% remote work and competitive USD compensation.

Qualifications

• 4+ years of experience in ML engineering or distributed systems.
• Expertise with distributed training frameworks (PyTorch DDP/FSDP/DeepSpeed).
• Hands-on CUDA programming and GPU profiling with Nsight.
• Strong English communication.

Responsibilities

• Implement and optimize distributed training with PyTorch DDP, FSDP, and DeepSpeed.
• Profile training performance and GPU utilization with Nsight.
• Design mixed-precision and memory-optimizations to boost throughput.
• Resolve cross-node coordination issues using CUDA and Triton.
• Scale huge models via parameter sharding and data sharding techniques.
• Collaborate with ML research teams to align infra with scaling needs.

Skills

Distributed training

PyTorch

Nsight profiling

CUDA optimization

English proficiency

Tools

PyTorch DDP

FSDP

DeepSpeed

Nsight

CUDA

Triton

TensorRT

Job description

BairesDev is hiring a Distributed Training Engineer to own scaling and optimization of training workloads across multi-GPU and multi-node environments. You will ensure peak performance for massive datasets and models with mixed-precision and distributed coordination.

You will collaborate with ML research teams and design infrastructure to support evolving model scaling requirements while enjoying 100% remote work and competitive USD compensation.