Senior Manager, Engineering - Enterprise AI and Automation

NVIDIA•Santa Clara, CA

1d•$272,000 - $431,250

About The Position

As a Senior Engineering Manager for Agentic Systems & Platform Architecture, you will lead the strategy and execution for NVIDIA’s agentic developer platform. You will understand how teams across the company build, evaluate, and improve autonomous agents, and turn evolving practices into scalable platform capabilities. You will identify gaps and friction, drive proof-of-concepts for emerging agent constructs and ecosystem tools, and operationalize effective approaches as reusable building blocks, integrations, and governance mechanisms that improve developer productivity and agent quality. The platform will help teams safely ship more autonomous systems at NVIDIA scale.

Requirements

Bachelor’s degree in Computer Science or Engineering, or equivalent experience.
10+ years of overall software engineering experience, including 4+ years managing high-performing teams.
Strong hands-on experience with evolving agent architectures and open-source libraries, and deep expertise in LLM and agent architectures, including leading proof-of-concepts and integrating them into real business use cases with measurable adoption or impact.
Ability to turn fast-moving, ambiguous problem spaces into clear platform strategy, roadmaps, and outcomes.
Proven track record building multi-team developer platforms, including APIs/SDKs, reusable components, and reference implementations.
Experience building evaluation and benchmarking systems for agent workflows, including metrics, regression, and feedback loops.
Strong judgment integrating open-source and third-party tools, with clear build-versus-use decision-making and integration strategy.
Product approach to safety and governance, including controls, auditability, monitoring, and risk management.
Strong leadership and executive communication skills across engineering, product, security, and research.

Nice To Haves

Experience implementing enterprise-grade governance for agent systems in production autonomous workflows, including controls, auditability, monitoring, and policy enforcement.
Demonstrated success taking new or open-source agent constructs from proof-of-concept to production adoption with clear business impact on cycle time, quality, cost, or reliability.
Experience building and scaling an agent platform or agent developer experience used by multiple teams, including SDKs, templates, reference applications, and reusable building blocks.
Clear perspective and practical examples of build-versus-use decisions, including when to adopt open-source or third-party tools versus building internal primitives, and how to operationalize the decision.
Deep experience with agent evaluation at scale, including long-horizon tasks, tool correctness, reliability testing, automated regressions, and offline/online feedback loops.

Responsibilities

Track and understand evolving agent development patterns across NVIDIA and the broader ecosystem.
Identify gaps and friction in agent architectures and translate findings into platform strategy that improves developer velocity and agent quality, supported by evaluations, benchmarking, and feedback loops.
Assess and integrate open-source and third-party tools where useful, and make build-versus-use decisions.
Architect and integrate high-performance data pipelines, RAG systems, vector databases, and GPU-optimized training and inference workflows.
Lead integration of the AI Data Platform into NVIDIA’s on-prem AI Factory, optimizing GPU-to-storage throughput, data locality, and distributed inference performance.
Establish and enforce Agent Governance policies covering model and tool usage, data lineage, compliance, and Responsible AI frameworks.
Design, implement, and maintain a centralized Agent Safety Toolkit with pre-vetted components for input/output guardrails and prompt injection defenses.
Lead and grow a high-performing team and a multi-functional community to standardize procedures and scale adoption.