Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which GPUs serve as the brains of computers, robots, and self-driving cars that can understand and interact with the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent.
NVIDIA is hiring a Senior Software Architect in the Agent Harness & Runtime Engineering team to build foundational systems for the next generation of agentic AI. Our team works at the intersection of AI and systems engineering, tackling challenges across agent runtimes, inference, evaluation, and large-scale execution. We are looking for someone with the AI breadth to quickly understand emerging problems, the systems depth to architect solutions at scale, and the engineering strength to build them. This is an opportunity to work across the agentic AI stack and translate rapidly evolving AI capabilities into scalable, production-quality systems.
What You'll Be Doing:
Architect and build scalable, reliable systems for agentic AI, spanning agent runtimes, harnesses, inference, evaluation, and orchestration.
Solve large-scale systems challenges across distributed execution, data/ETL pipelines, HPC, cloud, Kubernetes, and GPU compute environments.
Optimize systems for reliability, scalability, performance, resource utilization, and developer experience.
Prototype emerging ideas, write high-quality production code, and provide technical leadership across research, engineering, product, and infrastructure teams.
What We Need to See:
BS, MS, or equivalent experience in Computer Science, Computer Engineering, AI, or a related field, with 12+ years of relevant industry experience.
Strong foundation in modern AI, including LLMs, multimodal models, inference, agentic AI, and evaluation.
Deep expertise in software architecture, distributed systems, and large-scale systems design.
Strong hands-on programming skills in Python, C++, Go, Rust, or similar languages, with a track record of building high-quality production software.
Background building large-scale systems involving distributed execution, data processing/ETL, workflow orchestration, HPC, cloud, or GPU infrastructure.
Ability to reason across the stack—from AI model behavior and application logic to runtime, compute, and infrastructure.
Proven ability to take ambiguous, complex technical problems from architecture through implementation and production.
Ways to Stand Out from the Crowd:
Knowledge of LLM/VLM inference, model serving, model routing, or inference optimization.
Background in AI evaluation, benchmarking, experimentation, or large-scale AI infrastructure.
Track record of building reusable platforms supporting heterogeneous AI workloads and compute environments.
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
- BS, MS, or equivalent experience in Computer Science, Computer Engineering, AI, or a related field
- 12+ years of relevant industry experience
- Strong foundation in modern AI, including LLMs, multimodal models, inference, agentic AI, and evaluation
- Deep expertise in software architecture, distributed systems, and large-scale systems design
- Strong hands-on programming skills in Python, C++, Go, Rust, or similar languages
- Experience building large-scale systems involving distributed execution, data processing or ETL, workflow orchestration, HPC, cloud, or GPU infrastructure
- Ability to reason across AI model behavior, application logic, runtime, compute, and infrastructure
- Proven ability to take ambiguous, complex technical problems from architecture through implementation and production
- Knowledge of LLM or VLM inference, model serving, model routing, or inference optimization
- Background in AI evaluation, benchmarking, experimentation, or large-scale AI infrastructure
- Track record of building reusable platforms supporting heterogeneous AI workloads and compute environments
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.”
