Senior Systems Engineer -- Agentic AI

NVIDIA•Austin, TX

1d•$224,000 - $431,250

About The Position

NVIDIA is hiring for its Agent Harness & Runtime Engineering team to build foundational systems for the next generation of agentic AI. The team works across agent runtimes, inference, evaluation, and large-scale execution. The role calls for broad AI knowledge, deep systems expertise, and the ability to architect and build scalable, production-quality solutions as AI capabilities evolve. The engineer will work across the agentic AI stack, designing and implementing systems that support reliable, scalable execution and translate emerging AI capabilities into production systems. The position also involves prototyping ideas and providing technical leadership across research, engineering, product, and infrastructure teams.

Requirements

Bachelor’s degree, master’s degree, 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, with a track record of building high-quality production software.
Background building large-scale systems involving distributed execution, data processing or 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.

Nice To Haves

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.

Responsibilities

Architect and build scalable, reliable systems for agentic AI, including agent runtimes, harnesses, inference, evaluation, and orchestration.
Address large-scale systems challenges across distributed execution, data and ETL pipelines, HPC, cloud, Kubernetes, and GPU compute environments.
Optimize systems for reliability, scalability, performance, resource utilization, and developer experience.
Prototype emerging ideas and write high-quality production code.
Provide technical leadership across research, engineering, product, and infrastructure teams.
Take ambiguous, complex technical problems from architecture through implementation and production.