8 days ago
Remote, Spain +3 moreSenior
Responsibilities
- Build and deploy custom AI solutions on NeoCloud platforms and NVIDIA Cloud Partners, including distributed training, inference optimization, and MLOps pipelines.
- Act as the primary technical contact for internal and external customers and partners, guiding engagements and ensuring initiative success on DGX Cloud.
- Diagnose and solve full-stack AI and ML system problems in production and advise infrastructure experts on ML workload requirements.
- Profile and tune large-scale training and inference workloads to reduce latency, cost, and operational risk.
- Develop open-source tools and reference architectures for managing machine learning and AI workloads, pipelines, and systems at scale.
- Collaborate with teams building infrastructure software and accelerated frameworks, including support for new hardware and LLM performance evaluation.
Requirements
- BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
- 8+ years of experience in technical roles such as data science, data engineering, or ML engineering, ideally focused on large-scale production systems.
- Demonstrated AI/ML experience across multiple phases of the machine learning lifecycle, from exploratory analysis through production systems.
- Experience with Linux, batch schedulers, Kubernetes, distributed filesystems, and advanced datacenter-scale networking.
- Strong scripting and programming skills in Bash and Python, plus systems programming experience in C++, Go, or Rust.
- Experience using machine learning or deep learning frameworks for training and inference.
- Strong communication and technical presentation skills for explaining architectures, trade-offs, and recommendations to engineering and leadership audiences.
- Experience contributing to open-source communities and familiarity with the NVIDIA ecosystem, DGX systems, CUDA, NeMo, RAPIDS, Triton, NIM, InfiniBand, NVLink, and RoCE are preferred.
- Experience with security-critical machine learning systems, distributed training and inference frameworks, and cloud-native MLOps practices is preferred.
- Experience with containerization, CI/CD pipelines, workflow automation, observability stacks, GitOps workflows, and cross-layer performance or correctness troubleshooting is preferred.
Benefits
- Competitive salary and a generous benefits package.
- The base salary is determined by location, experience, and pay for similar positions; the posting provides Poland salary ranges for Level 4 and Level 5.
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.