Senior Deep Learning Engineer – Autonomous Vehicles
NVIDIA Corporation · Santa Clara, CA
$224,000–$356,500from the description
Jun 28, 2026
Santa Clara, CA
Jul 21, 2026
What this job asks for AI summary
A senior infrastructure engineering role focused on building and scaling the distributed training systems that underpin autonomous vehicle development. Day-to-day work involves developing libraries and frameworks for multi-thousand GPU clusters, optimizing the full training stack — from data loading to scheduling — and owning fault-resilient, high-availability components. Best suited to engineers with deep experience in large-scale ML infrastructure, distributed training, and systems-level work across HPC or datacenter environments.
Senior level · 12+ years · Santa Clara, CA · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 40 people in the Santa Clara, CA area plausibly meet what this posting asks for (software developers). range 8–85
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $213,110 (middle half $173,650–$226,080). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (8)
The role is primarily software/systems engineering (building and shipping distributed training libraries, orchestration layers, and infrastructure frameworks), which maps to Software Developers (15-1252). However, the strong emphasis on datacenter networking, parallel filesystems, and scheduler operations introduces a plausible 15-1244 signal — hence the runner-up.
The JD requires a BS, MS, or PhD 'or equivalent experience,' so no formal degree is hard-gated.
DDP and FSDP are listed together as a single distributed-training requirement; DDP is emitted as the primary skill with FSDP as an alternative. FSDP also appears separately under 'Ways to stand out' (preferred), so it is emitted again there as a standalone preferred skill.
RoCE (RDMA over Converged Ethernet) and InfiniBand are listed together as datacenter networking options; RoCE is emitted as primary with InfiniBand as the alternative.
Slurm and Kubernetes are listed as scheduler examples ('Slurm, Kubernetes, etc.'); Slurm is emitted as primary with Kubernetes as an alternative.
The 'Ways to stand out' section is framed as differentiating extras (preferred), not hard gates — skills appearing only there (large GPU cluster scaling experience, open-source contributions, fault resilience/elastic training, technical leadership, RL at scale) are marked preferred.
12+ years is stated at the role level against distributed systems broadly, not tied to any single named technology, so it is captured as the overall experience minimum.
The salary range ($224,000–$356,500/year) is location-dependent; NVIDIA's headquarters is in Santa Clara, CA, used as the primary CBSA. The posting does not explicitly state a single work location, but no remote option is mentioned.
Read the full posting
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