- Location
- US, CA, Santa Clara, United States of America
- Type
- Full-time
- Department
- Engineering
- Education
- PhD
- Source
- Workday
Description
NVIDIA is seeking an outstanding Applied AI Builder, Enterprise Agent Systems to join our team building production-grade AI applications for the enterprise. In this role, you will lead by example as both a hands-on developer and technical expert, building proof-of-concept solutions, reference architectures, and deployable single-agent and multi-agent systems that solve real business problems.
The Partner Solutions Architect team is dedicated to enabling ecosystem partners to build category-defining enterprise AI systems. We work across application design, orchestration, integrations, observability, and deployment to help enterprise developers to move from prototype to real-world impact using NVIDIA accelerated computing platforms. We welcome builders from all backgrounds who want to ship applied AI systems that perform reliably at scale.
What you'll be doing:
Build applied AI applications and agentic systems that solve real enterprise problems across functions and industries
Design single-agent and multi-agent workflows for tool use, retrieval, memory, planning, handoffs, and human-in-the-loop execution.
Build full-stack systems that move from prototype to secure production deployment, including APIs, orchestration, observability, evaluation, identity, and rollback.
Integrate with enterprise systems such as document stores, internal tools, codebases, data platforms, workflow engines, and business applications.
Use coding agents such as Codex, Claude Code, Open/NemoClaw, or similar OSS tools as part of implementation, testing, debugging, refactoring, and release workflows to scaling partners.
Define evals and feedback loops—including synthetic data generation and synthetic evaluation where useful—for task completion, reliability, latency, safety, cost, and measurable business impact.
MS or PhD degree in Computer Science/Engineering, Machine Learning, Data Science, Electrical Engineering or a closely related field.
What we need to see:
MSc, PhD in Computer Science, Electrical Engineering, Software Engineer, ML Engineer, or related fields (or equivalent experience).
5+ years of relevant work experience in developing and deploying AI models at scale as a Software Engineer or Deep Learning engineer or Solutions Architect
Evidence that you have built and shipped applied AI applications, enterprise copilots, agentic workflows, or automation systems that people actually use.
Strong understanding of foundation model behavior in real systems, including prompting, context engineering, retrieval, tool use, fine-tuning tradeoffs, and evaluation.
Real experience with multi-agent workflows, orchestration patterns, or complex long-running task systems.
Strong programming skills in Python plus at least one of TypeScript, Go, Rust, or C++.
Experience with synthetic data generation and evaluation, including synthetic tasks, traces, or test corpora used to improve coverage, quality, or robustness.
Familiarity with GPU-backed inference systems, performance tradeoffs, and cost-quality tradeoffs.
High agency, strong ownership, and a bias toward shipping.
Ways to stand out from the crowd:
Meaningful OSS contributions in agents, enterprise integrations, evals, observability, or developer tooling.
Experience deploying AI systems into enterprise, security-conscious, or regulated environments.
Experience with secure execution, sandboxing, permissioned tool use, secrets handling, or auditability.
Strong examples of multi-agent coordination in production.
Familiarity with MCP, A2A-style communication patterns, or advanced agent interoperability.
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 a diverse 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.
