Senior AI Systems Engineer – Large-Scale Distributed Training

Cerence AI; United States; Hybrid; USD 141,000 - 226,000; Full time

Benefits offered by this job

Annual bonus

Medical, dental, vision insurance

Paid time off

Paid holidays

Company 401K

Equity awards

Remote or hybrid work

Job summary

Cerence AI is seeking an ML Systems Engineer to design and operate distributed training infrastructure for large neural networks. You will optimize multi-node, multi-GPU execution, diagnose bottlenecks, and partner with research and applied ML teams to productionize large-model training pipelines.

You’ll work on memory and performance optimization, ensure training stability at scale, and contribute to faster iteration cycles across Cerence AI’s global initiatives.

Qualifications

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

Responsibilities

• Design and operate distributed training systems for large neural networks across GPU clusters.
• Optimise multi-node, multi-GPU execution to maximise throughput and utilization.
• Diagnose bottlenecks across compute, memory, and network.
• Improve training stability and fault tolerance at scale.
• Partner with research and applied ML teams to productionize large-model training pipelines.

Skills

Distributed systems

GPU clusters

PyTorch Distributed

Parallelism strategies

Low-level GPU networking

Tools

Slurm

Kubernetes

Ray

RunAI

NCCL

Job description

Cerence AI is seeking an ML Systems Engineer to design and operate distributed training infrastructure for large neural networks. You will optimize multi-node, multi-GPU execution, diagnose bottlenecks, and partner with research and applied ML teams to productionize large-model training pipelines.

You’ll work on memory and performance optimization, ensure training stability at scale, and contribute to faster iteration cycles across Cerence AI’s global initiatives.