Senior Deep Learning Software Infrastructure Engineer - Remote
$224,000 - $431,250 USD yearly
Originally posted 6 August 2026 by the employer.
Propel NVIDIA’s Autonomous Vehicles project forward.
About the role
This role involves building and scaling training libraries and infrastructure for NVIDIA’s Autonomous Vehicles project, enabling end-to-end autonomous driving models.
What you'll do
- Craft, scale, and harden deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
- Improve efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.
- Build robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
- Collaborate with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
- Own core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
- Partner with leadership to ensure infrastructure scales with growing GPU capacity and dataset size while maintaining developer efficiency and stability.
What you'll need
- 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 in deep learning frameworks (PyTorch), large scale training (DDP/FSDP, NCCL, tensor/pipeline parallelism), and performance profiling.
- Strong systems background: datacenter networking (RoCE, IB), parallel filesystems (Lustre), storage systems, schedulers (Slurm, Kubernetes).
- Proficiency in Python with experience writing production-grade libraries, orchestration layers, and automation tools.
- Ability to work closely with multi-functional teams (ML researchers, infra engineers, product leads) and translate requirements into robust systems.
Nice to have
- Experience scaling large GPU training clusters with >1,000 GPUs.
- Expertise in fault resilience and high availability, including elastic training and large-scale observability.
- Leadership skills as a hands-on technical authority, encouraging others and establishing guidelines for ML systems engineering.
Skills: deep learning infrastructure, multi-thousand GPU clusters, distributed training, datacenter networking, storage systems
This role has been open 71 days — well above the 51-day median for Infrastructure/Platform Software roles.
Infrastructure/Platform Software · Infrastructure Platform
|
Open roles in category
962
|
Median days open
51 d
|
Median salary
$236k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | NVIDIA | All employers we track in this specialty (962 roles · 86 employers) |
|---|---|---|
| Open roles in this specialty | 274 | 962 |
| Open roles in the wider Software, Firmware & Systems family | 973 | 6114 · 145 employers |
| Median days open | 46 d | 51 d (−5 d vs this employer) |
| Median salary (USD postings) | — | $236k |
Skills observed across this category: deep learning infrastructure, multi-thousand GPU clusters, distributed training, datacenter networking, storage systems
Who's hiring in this category
- NVIDIA (this employer) · 274 open roles · median 47 d
- Qualcomm · 62 open roles · median 53 d
- AMD · 54 open roles · median 19 d
- Graphcore · 44 open roles · median 93 d
- Intel Corporation · 43 open roles · median 12 d
- Cerebras · 36 open roles · median 83 d
How we counted: 962 open Infrastructure/Platform Software (Infrastructure Platform) roles from 86 employers tracked in the SemiconductorJobs index, counted 18 Oct 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.