2 days ago
Responsibilities
- Build and deploy custom AI solutions, distributed training, inference optimization, and MLOps pipelines on NeoCloud platforms, NVIDIA Cloud Partners, and DGX Cloud.
- Act as the primary technical contact for internal and external customers and partners, guiding engagements and resolving complex production problems.
- Work with infrastructure software and accelerated-framework teams supporting AI applications.
- Profile and tune large-scale training and inference workloads to reduce latency, cost, and operational risk.
- Develop open-source tools and reference architectures for building and managing machine learning and AI workloads at scale.
- Diagnose and resolve performance or correctness issues spanning hardware, networking, accelerators, hypervisors, operating systems, compilers, runtimes, application code, and libraries.
Requirements
- BS, MS, or PhD in Computer Science, Computer Engineering, 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 involving large-scale production systems.
- Demonstrated AI/ML experience across multiple phases of the machine learning lifecycle, from exploratory analysis through production systems.
- Knowledge of Linux, batch schedulers, Kubernetes, distributed filesystems, and advanced datacenter-scale networking.
- Strong scripting and programming skills with languages such as Bash and Python, plus systems programming experience with 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.
- Record of disciplined engineering execution on individual and collaborative projects.
- Preferred experience contributing to open-source communities and working with the NVIDIA ecosystem, including DGX systems, CUDA, NeMo, RAPIDS, Triton, NIM, InfiniBand, NVLink, and RoCE.
- Preferred familiarity with distributed training and inference frameworks, security-critical ML systems, and cloud-native MLOps practices including containerization, CI/CD pipelines, workflow automation, observability stacks, and GitOps workflows.
Benefits
- Competitive salary and a generous benefits package.
- Base salary varies by location, experience, and comparable employee pay; in Poland, the stated base salary ranges are PLN 292,500–507,000 for Level 4 and PLN 375,000–650,000 for Level 5.
Categories
About Nvidia
Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, ignited the era of modern AI and is fueling the creation of the metaverse. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry.