Senior Systems Engineer – Agentic AI

🔥 0 minutes ago

🏄 California, Texas – Remote

🏄 California – Remote 🤠 Texas – Remote

💵 $224k - $431.3k / year

⏰ Full Time

🟠 Senior

⚙️ Systems Engineer

🦅 H1B Visa Sponsor

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👻 Ghost score 1%

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Architect and build scalable, reliable systems for agentic AI across agent runtimes, harnesses, inference, evaluation, and orchestration • Solve large-scale systems challenges across distributed execution, data/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

🎯 Requirements

• BS, MS, 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 • Track record of building high-quality production software • Background building large-scale systems involving distributed execution, data processing/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 • Knowledge of LLM/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

🏖️ Benefits

• Equity • Benefits