Summer 2027

Solutions Architecture Intern

Summer 2027

Posted on 9/11/2026

Deadline 9/15/26
NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

Compensation Overview

$20 - $71/hr

Company Historically Provides H1B Sponsorship

Santa Clara, CA, USA

In Person

Bachelor's, Master's, PhD

Category
Solution Engineering
Required Skills
LLM
Graphics Processing Unit (GPU)
Kubernetes
Python
High Performance Computing (HPC)
Data Science
Neural Networks
CUDA
PyTorch
Machine Learning
Computer Networking
Data Engineering
Docker
C/C++
Robotics
DevOps
Linux/Unix
Reinforcement Learning

Get referred to NVIDIA

See people who can refer or advise you

Requirements
  • Pursuing a Bachelor of Science, Master of Science, or PhD in Computer Architecture, Computer Networking, Computer Engineering, Electrical Engineering, Computer Science, Mathematics, Physics, Data Science, or a related technical field.
  • Strong skills in one or more programming languages, including Python, C, or C++.
  • Ability to work independently and with a cross-functional team.
  • Curiosity about AI infrastructure and the modern AI stack, including large language models, inference serving, agentic workloads, or retrieval workloads, is preferred.
  • Strong analytical and problem-solving skills.
  • Familiarity with Linux system administration, Python, and networking concepts for AI Factory Deployment projects.
  • Familiarity with data center architectures and the Layer 2 through Layer 7 networking protocol stack for AI Factory Networking projects.
  • Understanding of GPU clustering, leaf-spine topologies, Remote Direct Memory Access, and RDMA over Converged Ethernet.
  • Understanding of GPU server architecture, containerized Docker and Kubernetes orchestration, Slurm-based workload orchestration, and modern inference-serving or agentic/large language model frameworks is preferred.
  • A strong foundation in deep learning fundamentals, neural network architectures, and advanced optimization techniques for AI Models and AI Agents projects.
Responsibilities
  • Collaborate with solution architects and engineering or product teams.
  • Understand the technical needs of partners and customers.
  • Develop proof-of-concept projects with NVIDIA technologies.
  • Help stand up cluster monitoring and telemetry, track utilization and reliability, and automate parts of the bring-up and health-checking process.
  • Help build agentic solutions to optimize operations, validate cloud hosting capacity, optimize inference platforms on customized AI Factory hardware, and automate workflows from bill of materials through equipment delivery.
  • Troubleshoot network performance, perform packet-level debugging, tune InfiniBand or RDMA over Converged Ethernet fabrics for Remote Direct Memory Access workloads, and bring up entire network fabrics.
  • Interact with engineering, product, and business development teams to help partners leverage NVIDIA technologies for training, fine-tuning, inference, retrieval, and agentic workloads.
  • Develop full-stack skills across GPU and networking systems, cluster orchestration, and production-ready AI Factory deployments.
  • Work on model optimization and build performance and reliability health-check recipes alongside senior Solution Architects on customer workloads.
  • Produce reference architectures, benchmarks, or sizing guides for reuse by other teams.
  • Deploy, train, customize, and optimize AI models in agentic workflows across vision, language, and voice modalities.
  • Improve AI model accuracy and performance on challenging datasets and help train partners.
  • Work on robotics reinforcement learning, vision-language-action models, motion planning, physics-based simulation, and software-in-the-loop testing.
  • Build customer-facing examples for synthetic data generation or robotics foundation-model training and analyze AI robotics workload performance.
  • Work on large-scale training and data pipelines, multi-sensor suites, and industrial or consumer scenarios.
  • Develop and demonstrate geospatial, signal, radar, and radio-frequency processing pipelines as proofs of concept.
  • Fuse multimodal sensor inputs into a digital twin and use it to plan and predict, train robots, and support other applications.
Desired Qualifications
  • Experience with NVIDIA GPUs and software libraries.
  • Work experience within an engineering or research community.
  • A computer architecture, networking, data science, or machine-learning foundation with Linux skills.
  • System administration or hands-on server or rack-level hardware experience and workload orchestration.
  • Academic or industry familiarity with GPUs, artificial intelligence, CUDA, or related technologies.
  • Experience with data center infrastructure from hardware through workloads running on NVIDIA AI Factories.
  • Working knowledge of Slurm and data science.
  • Background in network engineering and design, server-level infrastructure or systems work, and operations tooling focused on automation.
  • Hands-on experience with data center architectures, on-premises and cloud-based infrastructure, and system-level hardware and software.
  • Familiarity with Docker, Kubernetes, CUDA, PyTorch, JAX, modern inference frameworks, or agentic frameworks.
  • Familiarity with robotics tools such as the Robotics Operating System and simulation frameworks such as Isaac Sim.
  • Experience training or working with vision-language-action models, robotics competitions or labs, and hands-on pipeline optimization.

NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

Get referred to NVIDIA

See people who can refer or advise you

Simplify Jobs

Simplify's Take

What believers are saying

  • August 2026 revenue hit $96.2 billion; data center revenue reached $89.0 billion.
  • Management guided Q3 fiscal 2027 revenue to $108 billion, showing relentless demand.
  • NAVER, Brookfield, and Nvidia expanded Korea’s AI factory to 200 megawatts in July 2026.

What critics are saying

  • Taiwan indicted nine people on August 24, 2026 for illegal Nvidia chip exports to China.
  • Memory costs cut Q3 gross margin guidance to 71%-72%, pressuring profitability in 2026.
  • China export controls can erase a major market if Washington tightens rules further.

What makes NVIDIA unique

  • CUDA, Blackwell, and Nemotron lock developers into Nvidia’s full-stack AI ecosystem.
  • AWS and Nvidia announced 2 million additional GPUs for 2027-2028, deepening platform dependence.
  • NVIDIA DSX combines chips, software, and facilities for sovereign AI factories.

Help us improve and share your feedback! Did you find this helpful?

Benefits

Company Equity

401(k) Company Match

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

-1%

2 year growth

-2%
Yahoo Finance
Sep 9th, 2026
Nvidia CEO counters Michael Burry's AI chip depreciation bear case with market evidence

Nvidia CEO Jensen Huang has countered hedge fund manager Michael Burry's concerns about the useful life of AI semiconductors. Burry, known for predicting the 2008 subprime market crash, argues that companies overestimate GPU lifespans at six years rather than two to three years, thereby inflating profits. However, Nvidia's Ampere A100 chips, launched in mid-2020, remain in demand six years later. Neocloud CoreWeave recently contracted to rent these processors through 2029, nine years after their introduction. Rental prices for the newer Hopper H100 chips have increased 22% month over month, suggesting continued strong demand. Huang shared this pricing data on social media this week, reinforcing his position against Burry's bearish outlook on AI semiconductor longevity.

Yahoo Finance
Sep 9th, 2026
Nvidia raises Q3 revenue guidance to $108B, but margin squeeze from memory costs dampens investor reaction

Nvidia guided fiscal Q3 2027 revenue to $108 billion, an 18.7% increase from the prior quarter's guidance. The company expects revenue to grow approximately 70% in fiscal 2028, though supply constraints remain a factor. The stock gained 7% following the announcement, adding roughly $14.93 per share. However, rising memory costs driven by AI infrastructure demand are squeezing margins. Nvidia guided gross margin 0.9 percentage points lower for Q3, expecting it to bottom at 71-72% in Q4 2027 before stabilising at 72-73% in fiscal 2028. Operating expenses were also guided higher. The company's data centre business shows strong demand, with its ACIE segment growing 138% year-over-year to $40 billion in Q2 2027.

Yahoo Finance
Sep 9th, 2026
NVIDIA and CU Healthcare Innovation Fund invest in Verily Health's AI platform expansion

Verily Health has secured additional investment from NVIDIA and existing backer CU Healthcare Innovation Fund II. The funding extends Verily's March fundraising round. Current investors include Alphabet, Series X Capital, and UCHealth. The investment supports scaling of Verily's Pre Platform, a data and AI platform helping healthcare organisations deploy AI across research and care. It also backs Verily Me, the company's patient engagement offering. Verily and NVIDIA previously announced a collaboration in October 2025. Researchers using the Pre Platform can leverage NVIDIA AI libraries and B200 GPUs. Verily developed Forecast 1.0, a foundation model integrating genomics and electronic health record data to predict long-term health risks, using NVIDIA's AI stack.

Yahoo Finance
Sep 9th, 2026
NVIDIA stock soars 14,700% in decade as CEO confirms demand outstrips supply through 2028

NVIDIA has delivered a 14,460% return over the past decade, turning $10,000 into approximately $1.45 million. The company's stock currently trades near $226.18, with analysts setting a 12-month price target of $308.85, implying 36.55% upside. NVIDIA reported fiscal Q2 FY27 revenue of $96.22 billion, up 105.85% year-over-year, with data centre revenue hitting $89.02 billion. The company projects Q3 revenue of $108 billion at 74% gross margins. CEO Jensen Huang stated that demand significantly exceeds NVIDIA's current supply capacity. Management guided fiscal 2028 revenue growth of approximately 70%, describing it as supply-constrained. Top-five hyperscaler capital expenditure is projected at nearly $800 billion in 2026 and $1.3 trillion in 2027.

Fortune
Sep 9th, 2026
Nvidia CEO declares AGI achieved with OpenAI's Astra, but markets remain unmoved

Nvidia CEO Jensen Huang declared over the weekend that artificial general intelligence has arrived, citing OpenAI's newest model Astra. Markets responded tepidly: Nvidia shares fell 2% whilst companies leveraged to OpenAI, like CoreWeave and SoftBank, saw gains. Gil Luria of D.A. Davidson suggests Nvidia is "too big to grow" despite reporting $96 billion in quarterly revenue. Economist Basil Halperin argues real interest rates, not stock prices, would signal true AGI. Real rates have risen three to four percentage points since 2021, partly due to AI infrastructure spending, but remain far from levels that would indicate transformative AI impact. Most experts expect AI to add only about half a percentage point to GDP growth. Halperin forecasts the next five years will resemble "the dot-com boom, but twice as fast and twice as hard.