1 day ago
Munich, GermanySenior
Responsibilities
- Build and own production-grade applied AI systems and agentic solutions for NVIDIA technical and solution development use cases.
- Design agentic workflows and supporting workflow services, APIs, retrieval systems, tool integrations, agent harnesses, automation, telemetry, operational controls, and human oversight.
- Design reliable services, APIs, workflow state, event-driven execution, and observability using systems such as Kafka, ClickHouse, and OTel-style patterns.
- Develop production software in Python and other relevant languages with testing, observability, CI/CD, documentation, and operational practices.
- Build performance and benchmarking workflows covering validation, regression testing, tracing, metrics, failure analysis, latency, throughput, reliability, resource usage, and AI inference behavior.
- Codify recurring applied AI patterns, product gaps, and field lessons into APIs, services, reference architectures, playbooks, test harnesses, and shared engineering components.
- Debug and support production software, infrastructure, AI models, data pipelines, inference services, and GPU-accelerated environments.
- Drive system design, technical planning, design reviews, code reviews, cross-team execution, and support for critical issues.
Requirements
- Bachelor's, master's, or PhD in Computer Science, Engineering, or AI/ML, or equivalent experience.
- At least 5 years of professional software engineering experience owning production systems or meaningful platform components.
- Hands-on production experience with LLMs, generative AI, RAG, agentic AI, MCP, or related intelligent AI technologies beyond simple prompting or notebooks.
- Strong Python skills and practical experience with at least one additional production language such as C++, Go, Rust, or TypeScript.
- Experience building distributed systems, backend services, data pipelines, workflow orchestration, APIs, or developer platforms in production environments.
- Strong system design and operational judgment covering reliability, latency, cost, security, privacy, scalability, debuggability, maintainability, performance analysis, benchmarking, profiling, or capacity evaluation.
- Excellent debugging, problem-solving, written communication, and verbal communication skills in English.
- Ability to own ambiguous cross-team engineering work and collaborate across US Pacific, EMEA, and APAC time zones.
- Preferred experience includes real-world AI implementations, agent tools, MCP-compatible modules, A2A-style bridges, agent frameworks, evaluation frameworks, or RAG systems used by real users.
- NVIDIA GPU, NVIDIA NIM, NeMo Agent Toolkit, CUDA, and agentic AI development framework familiarity is advantageous.
- Open-source contributions, technical papers, patents, conference talks, engineering blogs, or major internal engineering artifacts are advantageous.
Benefits
- NVIDIA offers highly competitive salaries and a comprehensive benefits package.
- The role involves collaboration across global teams and time zones.
- NVIDIA provides reasonable accommodation for individuals with disabilities during application, interviews, and employment.
Tech Stack
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.