Are you ready to translate groundbreaking AI research into secure, production-grade systems? Want to shape the next generation of AI agent infrastructure? Join us!
At NVIDIA OpenShell, we are building the runtime infrastructure for secure, scalable, production-grade AI agents. As an OpenShell Research Engineer, we will look to you to 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. We expect you to identify promising methods from academia, industry, open source, and internal NVIDIA research, understand where they matter for OpenShell, validate their impact through hands-on prototypes, benchmarks, and real agent workflows, and help us integrate the best ideas into the product.
What You’ll be Doing:
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.
What you’ll need:
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.
Ways to stand out from the crowd:
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.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us. If you're creative, passionate and self-motivated, we want to hear from you! NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- 8+ years of professional experience in research engineering, software development, or related technical field
- MS or PhD in Computer Science, Physics, or related field or equivalent experience
- Proven track record turning complex research into reusable products, tools, demos, benchmarks, or production systems at scale
- Deep experience in several of: 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 work, run experiments, form viewpoints, and communicate findings
- Strong product sense and care for developer UX and agent ergonomics
- Focus on real-world impact: translate research into enterprise capabilities, reference implementations, or product improvements
- Outstanding team orientation and comfort collaborating across research, engineering, product, design, and developer-facing teams
- Experience with secure agent runtimes, tool sandboxing, capability-based security, or enterprise policy systems
- Experience with compliance or enterprise governance (auditability, data retention, access control, SOC2, HIPAA, GDPR)
- Experience with LLM inference infrastructure or model serving using Triton, TensorRT-LLM, vLLM, SGLang, Ray, Kubernetes, or cloud GPU platforms
- Experience integrating inference backends into agent systems: routing across models, tool-aware context management, streaming, structured outputs, retries, monitoring, cost/performance optimization
- Experience developing or maintaining open-source software in AI agents, LLM systems, developer tooling, ML infrastructure, or model serving
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
