Scale deep learning training libraries and distributed infrastructure across clusters with thousands of GPUs for autonomous vehicle models.
Originally posted 6 November 2026 by the employer.
About the role
As a Senior Deep Learning Software Infrastructure Engineer on the Autonomous Vehicles project, you will build and scale training libraries and infrastructure for end-to-end autonomous driving models. The role focuses on enabling distributed training across clusters with thousands of GPUs and massive video datasets.
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, including data loaders, distributed training, scheduling, and performance monitoring.
- Build training pipelines and libraries to handle massive video datasets and enable experimentation.
- 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.
- Collaborate with researchers, model engineers, and platform teams to enhance efficiency, minimize stalls, and improve training availability.
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, ideally in ML, HPC, or large-scale data infrastructure.
- Extensive knowledge of deep learning frameworks (PyTorch preferred), large scale training (DDP/FSDP, NCCL, tensor/pipeline parallelism), and performance profiling.
- Strong systems background covering datacenter networking (RoCE, IB), parallel filesystems (Lustre), storage systems, and schedulers (Slurm, Kubernetes).
- Proficiency in Python with experience writing production-grade libraries, orchestration layers, and automation tools.
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.
- Hands-on technical leadership experience establishing guidelines for ML systems engineering.
Skills: Distributed Training, PyTorch, NCCL, GPU Clusters, Lustre, Slurm
More semiconductor jobs: NVIDIA jobs · California jobs
This role has been open 0 days — well below the 53-day median for Infrastructure/Platform Software roles.
Infrastructure/Platform Software · Infrastructure Platform
|
Open roles in category
1024
|
Median days open
53 d
|
Category median salary (all employers)
$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 (1024 roles · 93 employers) |
|---|---|---|
| Open roles in this specialty | 335 | 1024 |
| Open roles in the wider Software, Firmware & Systems family | 1223 | 6531 · 156 employers |
| Median days open | 57 d | 53 d (+4 d vs this employer) |
| Median salary (USD postings) | — | $236k |
Skills observed across this category: Distributed Training, PyTorch, NCCL, GPU Clusters, Lustre, Slurm
Who's hiring in this category
- NVIDIA (this employer) · 330 open roles · median 58 d
- Qualcomm · 58 open roles · median 60 d
- AMD · 55 open roles · median 35 d
- Graphcore · 48 open roles · median 100 d
- Intel Corporation · 38 open roles · median 23 d
- Arm Holdings · 37 open roles · median 10 d
How we counted: 1024 open Infrastructure/Platform Software (Infrastructure Platform) roles from 93 employers tracked in the SemiconductorJobs index, counted 7 Nov 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.