Software Engineer, OpenShell
$184k - $288k • Remote • US, Remote
Posted 1d ago
About the job
At NVIDIA OpenShell, we are building the runtime infrastructure for secure, scalable, production-grade AI agents. As an OpenShell Research Engineer, you will help bring the latest advances in agentic systems into the runtime, tools, and workflows that enterprise builders rely on. This role sits at the intersection of research, product, and engineering, requiring you to identify promising methods from academia and industry, validate their impact through prototypes and benchmarks, and help integrate the best ideas into the product.
Responsibilities
- Track the evolution of agentic systems, including research in tool use, planning, memory, evaluation, self-improvement, multi-agent workflows, runtime infrastructure, and agent safety/security.
- Identify research ideas that can meaningfully improve OpenShell and translate them into concrete product opportunities.
- Reproduce and test promising methods from papers, open-source projects, industry work, and internal research.
- Build rapid proof-of-concept prototypes using OpenShell, such as agent harnesses, evaluation loops, and self-improving workflows.
- Design evaluation and red-team harnesses to measure agent reliability, usefulness, scalability, safety, security, and developer experience.
- Design secure-by-default workflows for agents operating with tools, code, files, credentials, and enterprise systems.
- Collaborate with engineering, product, design, research, solutions, and developer-facing teams to advance 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.
- Strong background in translating 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, including surveying relevant work, running experiments, forming a clear point of view, and communicating findings clearly.
- Strong product sense and care for UX and AX.
- Focus on real-world impact, aiming for research to become enterprise capabilities, reference implementations, developer workflows, or product improvements.
- Outstanding team orientation and comfort collaborating across various teams.
Benefits
- Equity
- Benefits