Software Engineer, OpenShell

NVIDIA is a leading technology company focused on groundbreaking developments in Artificial Intelligence and High-Performance Computing. They are seeking a Senior Software Engineer to help build the runtime infrastructure for secure, scalable, production-grade AI agents, bridging the gap between research and product development.

Responsibilities:

• Track the cutting edge: How are agentic systems evolving? You'll follow research in tool use, planning, memory, evaluation, self-improvement, multi-agent workflows, runtime infrastructure, and agent safety/security
• Bridge research and product: Identify research ideas that can meaningfully improve OpenShell and translate them into concrete product opportunities
• Benchmark and adapt: Reproduce and test promising methods from papers, open-source projects, industry work, and internal NVIDIA research
• Build rapid prototypes: Create hands-on proof-of-concepts using OpenShell, including agent harnesses, evaluation loops, self-improving workflows, and runtime-native developer experiences
• Red-team systems: Design evaluation and red-team harnesses that measure agent reliability, usefulness, scalability, safety, security, and developer experience
• Secure the workflow: Help us design secure-by-default workflows for agents operating with tools, code, files, credentials, and enterprise systems
• Partner across teams: Collaborate closely with engineering, product, design, research, solutions, and developer-facing teams to move ideas from prototype to product

Requirements:

• 8+ years of professional practical experience in research engineering, software development, or a related technical field
• MS/PhD in Computer Science, Physics, or a related field or equivalent experience
• A strong background in turning complex research into reusable products, tools, demos, benchmarks, or production systems at scale
• Deep experience in several of the following: LLMs, agent harnesses, multimodal generative models, evaluation frameworks, synthetic data generation, post-training, inference infrastructure/optimization, adversarial ML, or agent safety/security
• Demonstrated ability to drive independent technical investigation: survey relevant work, run experiments, form a clear point of view, and communicate findings clearly
• Strong product sense and care for UX and AX: tools should be intuitive for developers and ergonomic for agents
• A focus on real-world impact: we want research to become enterprise capabilities, reference implementations, developer workflows, or product improvements
• Outstanding team orientation and comfort collaborating across research, engineering, product, design, solutions, and developer-facing teams
• Experience with secure agent runtimes, tool sandboxing, capability-based security, or enterprise policy systems
• Experience with compliance or enterprise governance requirements such as auditability, data retention, access control, SOC2, HIPAA, GDPR, or regulated deployment environments
• Experience with LLM inference infrastructure, model serving, or inference optimization using tools such as Triton, TensorRT-LLM, vLLM, SGLang, Ray, Kubernetes, or cloud GPU platforms
• Experience integrating inference backends into agentic systems, including routing across models, tool-aware context management, streaming, structured outputs, retries, monitoring, and cost/performance optimization
• Experience developing or maintaining open-source software in AI agents, LLM systems, developer tooling, ML infrastructure, model serving, or related areas