Senior Software Engineer, Applied AI

We are looking for a Senior Software Engineer, Applied AI Systems to build production AI / ML and agentic solutions. This hands‑on senior engineer will turn ambiguous technical problems into durable software systems and AI‑enabled systems such as agents, workflow services, APIs, data pipelines, tool integrations, evaluation and benchmarking harnesses, reference architectures, and operational tooling.

In this role you will build real AI systems as high‑quality software: write and review code, make architecture tradeoffs, benchmark behavior and performance, and support production solutions from prototype through validation, hardening, deployment, and ongoing support. You will help shape how NVIDIA’s applied AI systems are built, measured, and reused.

Collaboration across global teams and time zones will be essential for design reviews, planning, debugging, critical issue support, and technical decision‑making. You will drive execution across teams, turning complex requirements into clear technical plans and reusable software capabilities.

• Build and own production‑grade applied AI systems for NVIDIA’s technical and solution development use cases, including agentic solutions that materially improve systems and software.
• Design and build agentic workflows and the surrounding software: workflow services, APIs, retrieval, MCP/A2A‑style 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, Click House, and OTel‑style patterns.
• Translate complex technical and operational requirements into clear system designs, plans, interfaces, measurable outcomes, and pragmatic technical decisions through design reviews, code reviews, and clear communication.
• Develop production software in Python and other relevant languages, with strong testing, observability, CI/CD, documentation, and operational practices.
• Build performance and benchmarking workflows for existing production solutions or products, including validation harnesses, regression tests, tracing, metrics, failure analysis, latency, throughput, reliability, resource usage, and AI/inference behavior where relevant.
• Improve standard solution patterns alongside larger applied AI systems, working with NVIDIA engineering and solution teams to codify repeated patterns, product gaps, and field lessons into APIs, services, reference architectures, playbooks, test harnesses, and shared engineering building blocks.
• Debug and support production solutions across software, infrastructure, AI models, data pipelines, inference services, and GPU‑accelerated environments, turning recurring support patterns into product or platform improvements.

• BS, MS, or PhD in Computer Science, Engineering, AI/ML, or equivalent experience, with 5+ years of professional software engineering experience owning production systems or meaningful platform components.
• Hands‑on experience with LLM, generative AI, RAG, agentic AI, MCP or intelligent AI technologies beyond simple prompting or notebooks, including tool use, retrieval, evaluation, guardrails, orchestration, or human‑in‑the‑loop control.
• Strong Python engineering skills and practical experience with at least one additional production programming language such as C++, Go, Rust, or Type Script.
• Demonstrated ability to develop and build distributed systems, backend services, data pipelines, workflow orchestration, APIs, or developer platforms using production environments like Kafka, Click House, PostgreSQL, Redis, object storage, Kubernetes, or similar technologies.
• Strong system design and operational judgment, including reliability, latency, cost, security, privacy, scalability, debuggability, maintainability, performance analysis, benchmarking, profiling, or capacity evaluation.
• Excellent debugging and problem‑solving skills across software, infrastructure, AI systems, and performance bottlenecks.
• Proven ownership of ambiguous, cross‑team engineering work, with ability to collaborate with distributed teams spanning US Pacific, EMEA, and APAC…