AI role · Open offer
Senior ML Engineer — inference platform
NVIDIA
This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.
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At a glance
How you'll work, where, and key listing dates.
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- 2788 San Tomas Expy, Santa Clara, CA 95051, United States · Hybrid
- Compensation
- $200k–$280k/year + RSUs
- Experience level
- Senior
- Team
- AI inference
- Benefits & perks
About this role
What the team is looking for and how this position fits.
NVIDIA is hiring a Senior Senior ML Engineer — inference platform on the AI inference team (Hybrid · Santa Clara, United States). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.
You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
**NVIDIA** powers the AI revolution — GPUs, **CUDA**, **TensorRT**, and **Triton** inference from **Santa Clara** headquarters.
Senior ML Engineer (hybrid Santa Clara): optimize **LLM serving**, **multi-GPU scheduling**, and **edge deployment** for enterprise customers.
About NVIDIA
- World leader in accelerated computing; Santa Clara is global HQ.
- Teams partner with cloud providers and open-source communities.
- Dogfood latest GPU architectures on real production workloads.
What you will do
- Benchmark and tune inference stacks for transformer models.
- Build tooling for quantization and speculative decoding.
- Publish performance guides used by external developers.
Stack & environment
- Python, CUDA, PyTorch, Triton, Kubernetes.
- Hybrid Santa Clara; occasional travel to datacenter partners.
What we look for
- 5+ years ML systems or GPU kernel optimization.
- Authorized to work in the United States.
Skills we're hiring for
What each capability means for this role—not a keyword checklist.
- Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
- PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
- CUDA
Tools & stack
Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).
- KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
- DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.
You're a strong fit if…
Tailored to this listing—use it as a self-check before you apply.
- ✓You can show evidence in your application: Triton, TensorRT, or CUDA optimization with latency benchmarks.
- ✓Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, CUDA, C++, and Kubernetes.
- ✓You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
- ✓You are comfortable with the hybrid rhythm (Hybrid · Santa Clara, United States)—onsite collaboration when it matters, deep work when remote.
- ✓You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
- ✓
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What the employer asked for
Triton, TensorRT, or CUDA optimization with latency benchmarks.
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