This role pays more than
86%
of similar roles. Most pay
$151,356–$214,250
— the shaded band above.
At the midpoint, this role pays about
$236k
versus about
$183k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
909 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 893 roles with salary data.
As a Senior Software Engineer, Agentic Engineering within the Deep Learning Framework Group, you will build agentic workflows to automate code generation, testing, and tuning across various frameworks, compilers, and developer tooling. You will partner with internal engineering teams to identify friction points and translate complex requirements into durable, scalable infrastructure. Your daily work involves developing multi-agent orchestration using tools like LangChain or LangGraph, integrating systems into git-native workflows and CI pipelines, and potentially agent-ifying compiler infrastructure for high-dimensional optimizations on real hardware. You will utilize Python and possess a working knowledge of GPUs and data-parallel systems to solve problems at the intersection of AI and engineering craft. Required expertise includes experience with LLM-based workflow automation, program transformation, and hardware-aware performance optimization for deep learning workloads while navigating evolving requirements in a high-autonomy environment.
What does a Software Engineer earn in California?
Median $214000 from 778 postings across 66 companies.
Develop agentic workflows to automate code generation, testing, and tuning across NVIDIA's frameworks and compilers.
Identify friction points in internal engineering processes to determine where agentic workflows provide the highest impact.
Build multi-agent orchestration systems using tools like LangChain or LangGraph to drive autonomous loops.
Integrate these automated systems into git-native workflows and CI pipelines for testing on real GPUs.
Translate complex requirements from early-adopter teams into durable, scalable infrastructure.
Automate compiler infrastructure to enable agents to perform high-dimensional optimizations with closed-loop validation.
Develop reusable agentic methodologies to share across the broader organization.
What we're looking for
MS in Computer Science, Engineering, or equivalent experience.
6+ years of experience.
Strong Python development skills.
Working knowledge of GPUs or other highly data-parallel systems.
Demonstrated projects or work experience using and supporting AI systems.
Track record of shipping complex projects with minimal direction.
Experience building tools or systems shaped by direct partnership with internal customer or user teams.
Experience in multi-agent orchestration, compiler infrastructure, autonomous search, hardware-aware optimization, or code generation systems (preferred).
CUDA
Python
C++
Rust
GPU Programming
TRT-LLM
SGLang
vLLM
Transformer Engine
Reinforcement Learning
High-Performance Computing
Compilers
Deep Learning
Agentic AI
Multi-agent Systems
Inference Systems