Description
Join NVIDIA OpenShell as a Software Engineer, where you'll translate groundbreaking AI research into secure, production-grade systems. We're building the runtime infrastructure for secure, scalable, AI agents.
As an OpenShell Research Engineer, you'll identify promising methods from academia, industry, open source, and internal NVIDIA research. You'll validate their impact through hands-on prototypes, benchmarks, and real agent workflows, and help integrate the best ideas into the product.
Responsibilities:
- Track the cutting edge: Follow research in agentic systems, focusing on 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 improve OpenShell and translate them into product opportunities.
- Benchmark and adapt: Reproduce and test promising methods from papers, open-source projects, and internal research.
- Build rapid prototypes: Create proof-of-concepts using OpenShell, including agent harnesses, evaluation loops, and runtime-native developer experiences.
- Red-team systems: Design evaluation and red-team harnesses to measure agent reliability, usefulness, scalability, safety, security, and developer experience.
- Secure the workflow: Help design secure-by-default workflows for agents operating with tools, code, files, credentials, and enterprise systems.
- Partner across teams: Collaborate with engineering, product, design, research, solutions, and developer-facing teams to move ideas from prototype to product.
Requirements:
- 8+ years of professional experience in research engineering, software development, or a related technical field
- MS/PhD in Computer Science, Physics, or a related field or equivalent experience
- 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 and communicate findings clearly
- Strong product sense and care for UX and AX
- Focus on real-world impact
- Outstanding team orientation and comfort collaborating across research, engineering, product, design, solutions, and developer-facing teams
Nice to Have:
- Experience with secure agent runtimes, tool sandboxing, capability-based security, or enterprise policy systems
- Experience with LLM inference infrastructure, model serving, or inference optimization using tools like Triton, TensorRT-LLM, vLLM, SGLang, Ray, Kubernetes, or cloud GPU platforms
- Experience integrating inference backends into agentic systems
- Experience developing or maintaining open-source software in AI agents, LLM systems, developer tooling, ML infrastructure, model serving, or related areas