Senior Deep Learning Software Infrastructure Engineer

Nvidia

Confirmed live yesterday High trust

Remote

Quick summary

Work type: Remote
Location: Canada
Salary: $224,000–$356,500 / yr
Posted: 4 days ago
Freshness: Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $201k

This role $290k

$136k most similar roles pay here $380k

This role pays more than 97% of similar roles. Most pay $159,437–$241,750 — the shaded band above. At the midpoint, this role pays about $290k versus about $201k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Senior Deep Learning Software Infrastructure Engineer

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As a Senior Deep Learning Software Infrastructure Engineer, you will join the Autonomous Vehicles project to build and scale training libraries and infrastructure for end-to-end autonomous driving models. You will be responsible for crafting, scaling, and hardening deep learning infrastructure frameworks for multi-thousand GPU clusters while improving efficiency across data loaders, distributed training, scheduling, and performance monitoring. Your daily work involves building robust pipelines to handle massive video datasets and owning core components like orchestration libraries and fault-resilient systems. The role requires expertise in PyTorch, DDP/FSDP, NCCL, tensor/pipeline parallelism, and Python for production-grade tools. You will also leverage knowledge of datacenter networking including RoCE and IB, parallel filesystems like Lustre, and schedulers such as Slurm or Kubernetes to solve complex problems related to large-scale training and high availability in a distributed environment.

Skills

PyTorch Python Distributed Training DDP FSDP NCCL Pipeline Parallelism Kubernetes Slurm RoCE Lustre Performance Profiling Observability

What you'll do

Build and scale deep learning infrastructure libraries for training on multi-thousand GPU clusters.
Optimize the training stack including data loaders, distributed training, scheduling, and performance monitoring.
Develop robust pipelines to handle massive video datasets and enable rapid experimentation.
Own core components such as orchestration libraries, distributed training frameworks, and fault-resilient systems.
Improve training availability by minimizing stalls and enhancing infrastructure efficiency.
Ensure infrastructure scales with growing GPU capacity while maintaining developer stability and efficiency.

What we're looking for

BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, or a related field, or equivalent experience.
12+ years of professional experience building and scaling high-performance distributed systems in ML, HPC, or large-scale data infrastructure.
Extensive knowledge of deep learning frameworks (PyTorch preferred) and large-scale training techniques like DDP, FSDP, NCCL, and parallelism.
Strong systems background including datacenter networking (RoCE, IB), parallel filesystems (Lustre), storage systems, and schedulers (Slurm, Kubernetes).
Proficiency in Python for writing production-grade libraries, orchestration layers, and automation tools.
Experience scaling large GPU training clusters with more than 1,000 GPUs.
Expertise in fault resilience, high availability, elastic training, and large-scale observability.
Demonstrated leadership skills as a hands-on technical authority to establish guidelines for ML systems engineering.

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