Senior Principal AI Engineer Distributed Training Architect

Cerence Inc.; United States; Remote; USD 180,000 - 260,000; Full time

Job summary

Cerence Inc. is seeking an ML Systems Engineer to design and operate distributed training infrastructure for large neural networks across GPU clusters. You will optimize multi-node, multi-GPU execution and diagnose bottlenecks in compute, memory, and networking.

You will collaborate with research and applied ML teams to productionize training pipelines, manage multi-node launches, handle failure recovery, and tune performance for reliability at scale.

Qualifications

• Hands-on experience with distributed systems or ML systems.
• Experience running large-scale workloads on GPU clusters.
• Production experience with PyTorch distributed training.
• Understanding of data, tensor, and pipeline parallelism.

Responsibilities

• Design and operate distributed training systems for large neural networks across GPU clusters.
• Optimize multi-node, multi-GPU execution for throughput.
• Diagnose & resolve bottlenecks across compute, memory, and network.
• Partner with research and applied ML teams to productionize training pipelines.
• Own multi-node launch configurations, failure recovery, and performance tuning.

Skills

Distributed systems

PyTorch distributed training

Parallelism strategies

GPU networking

High performance computing

Tools

Slurm

Kubernetes

Ray

RunAI

NCCL

RDMA

InfiniBand

NVLink

Megatron-LM

DeepSpeed

Job description

Cerence Inc. is seeking an ML Systems Engineer to design and operate distributed training infrastructure for large neural networks across GPU clusters. You will optimize multi-node, multi-GPU execution and diagnose bottlenecks in compute, memory, and networking.

You will collaborate with research and applied ML teams to productionize training pipelines, manage multi-node launches, handle failure recovery, and tune performance for reliability at scale.