Distributed AI Training Architect

cerence; United States; On-site; USD 180,000 - 250,000; Full time

Job summary

cerence is seeking an experienced ML infrastructure engineer to design and operate distributed training systems for large neural networks across GPU clusters. You will optimize multi-node, multi-GPU execution and diagnose bottlenecks across compute, memory, and networking to improve training efficiency.

Ideal candidates have hands-on experience with distributed systems, PyTorch distributed training, and deep knowledge of GPU communication.

Qualifications

• Hands-on experience with distributed or ML systems at scale.
• Experience running large-scale workloads on GPU clusters.
• Production experience with PyTorch distributed training.
• Strong understanding of data, tensor, and pipeline parallelism.
• Low-level understanding of GPU communication and networking.

Responsibilities

• Design and operate distributed training systems for large neural networks across GPU clusters.
• Optimize multi-node, multi-GPU execution for throughput and utilization.
• Diagnose bottlenecks across compute, memory, and network.
• Improve training stability and fault tolerance at scale.

Skills

Distributed systems

GPU clusters

PyTorch distributed

Parallelism strategies

GPU networking

Tools

Slurm

Kubernetes

Ray

RunAI

NCCL

RDMA

InfiniBand

NVLink

Megatron-LM

DeepSpeed

Job description

cerence is seeking an experienced ML infrastructure engineer to design and operate distributed training systems for large neural networks across GPU clusters. You will optimize multi-node, multi-GPU execution and diagnose bottlenecks across compute, memory, and networking to improve training efficiency.

Ideal candidates have hands-on experience with distributed systems, PyTorch distributed training, and deep knowledge of GPU communication.