# Senior Software Engineer, Agentic Systems at NVIDIA

NVIDIA is hiring a Senior Software Engineer, Agentic Systems in Santa Clara, United States. It pays $184k-$288k a year and Level rates it Builds AI ●●●●; you can [apply on NVIDIA](https://jobsbylevel.com/go/f74a93e4-13fe-4423-b403-b0bd4925c53b?utm_medium=ai-agent).

AI Level 4, AI centrality 91 out of 100. US, CA, Santa Clara.

## Details

- Company: [NVIDIA](https://jobsbylevel.com/companies/nvidia)
- AI level: AI Level 4 (score 91 out of 100)
- Location: US, CA, Santa Clara
- Salary: $184k-$288k
- Posted: November 6, 2026
- Apply: https://jobsbylevel.com/go/f74a93e4-13fe-4423-b403-b0bd4925c53b?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-software-engineer-agentic-systems-at-nvidia-e1bec9

## Prepare for this job

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## Description

We are looking for a Senior Software Engineer to help build NeMo Platform, NVIDIA’s product for developing, evaluating, deploying, and operating AI systems at scale. This role will focus on NeMo Evaluator, which helps teams understand whether changes to AI agents are making those agents better. As AI systems become more autonomous and more deeply integrated into real workflows, teams need practical infrastructure for observing behavior, measuring progress, catching regressions, and iterating with confidence. Our roadmap is increasingly focused on agentic development and automated agent improvement: giving teams the infrastructure they need to compare versions, understand behavior, and make empirically grounded improvements over time. What you'll be doing: Design and implement Python-first APIs, SDK workflows, and plugin interfaces for building, measuring, and improving agents across multiple runtimes and product surfaces Build reusable systems for observing behavior, measuring progress, detecting regressions, and turning runtime evidence into product decisions Build systems for ingesting, normalizing, validating, and analyzing agent execution data and evaluation datasets Partner with research, product, platform, and infrastructure teams to integrate agentic capabilities broadly across NVIDIA agent runtimes and developer workflows Help turn emerging agent development and improvement techniques into reliable, reusable product capabilities Improve reliability, observability, debuggability, and performance across NeMo Platform, SDKs, plugins, jobs, and developer workflows Build strong test coverage across unit, integration, E2E, Docker, and Kubernetes workflows Drive “speed of light” engineering: fast iteration, high ownership, pragmatic decisions, and performance-minded implementation under production constraints Provide senior technical leadership through design reviews, code reviews, mentoring, and ownership of ambiguous cross-component problems What we need to see: BS, MS, or equivalent experience in Computer Science, Computer Engineering, or a related technical field 5+ years of professional software engineering experience building production systems Excellent Python engineering skills, including API design, typing, testing, debugging, performance analysis, and maintainable software design Experience designing SDKs, libraries, plugins, CLIs, or other developer-facing interfaces Experience with distributed systems, cloud-native services, containers, Kubernetes, or job orchestration Strong understanding of reliability, scalability, security, and performance tradeoffs in production infrastructure Experience with structured data modeling and validation systems such as Pydantic, typed schemas, event/trace models, or SDK-generated types Ability to work independently, define technical scope, break down ambiguous problems, and drive work across team boundaries Clear communication skills and a track record of collaborating with engineering, product, research, or customer-facing teams Ways to stand out from the crowd: Experience building, deploying, and iterating on production agentic AI systems where evaluation was used to measure and improve real product outcomes Experience designing evaluation workflows for heterogeneous agents, including tool-using agents, RAG agents, workflow agents, coding agents, or long-running autonomous systems Experience integrating evaluation capabilities across multiple products, runtimes, or internal platforms, especially through Python SDKs, plugins, or shared developer tooling Strong ability to connect technical evaluation work to business outcomes, product quality, user experience, reliability, or operational efficiency Experience with enterprise AI systems where measurement, regression testing, observability, governance, and continuous improvement are required for production deployment NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most

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