Senior AI Distributed Systems Engineer

United States Digital Space LLC; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Cerence Inc. is seeking an 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 in compute, memory, and networking to maximize throughput and training stability. You will partner with research and applied ML teams to productionize large-model training pipelines using PyTorch Distributed, Megatron-LM, and DeepSpeed, with emphasis on scalable,

Qualifications

• Strong hands-on experience with distributed ML systems.
• Experience running large-scale workloads on GPU clusters.
• Production experience with PyTorch distributed training.
• Understanding of parallelism strategies (data, tensor, pipeline).

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.
• Collaborate with R&D teams to productionize training pipelines.

Skills

Distributed systems

Large-model training

GPU clusters

PyTorch Distributed

Megatron-LM

DeepSpeed

NCCL

Kubernetes

Slurm

Ray

Tools

Slurm

Kubernetes

Ray

RunAI

NCCL

RDMA

InfiniBand

NVLink

PyTorch Distributed

Megatron-LM

DeepSpeed

Job description

Cerence Inc. is seeking an 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 in compute, memory, and networking to maximize throughput and training stability. You will partner with research and applied ML teams to productionize large-model training pipelines using PyTorch Distributed, Megatron-LM, and DeepSpeed, with emphasis on scalable,