Senior AI Application Developer - GPU and SOC Architecture Modeling

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which the GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As a Developer Technology Engineer you will be at the forefront of innovation, working with leading industry partners and exciting OSS projects to accelerate RTX & DGX SoC performance for agentic use. This role offers an outstanding opportunity to collaborate with world-class talent and make a significant contribution to the next era of enterprise and consumer AI. What you'll be doing: Own key engagements with our fast-paced developer ecosystem partners, optimizing their applications to deliver outstanding end-to-end performance on RTX and DGX SoCs. Take ownership of performance optimization for compute-intensive CPU, AI, and 3D graphics workloads, working with domain experts to identify bottlenecks and implement effective solutions. Work across system software, GPU driver, architecture, and NVIDIA Research teams to influence next-generation, high-performance SoC platforms by bringing real-world workflows and actionable insights from partner and customer needs. Provide technical guidance and mentorship to junior engineers while contributing to an inclusive and high-performing team environment. What we need to see: BS or MS degree in Computer Science, Engineering, Mathematics or related degree (or equivalent experience) 5+ years of respective work experience as software developer Proficiency in C/C++, Python, software

NVIDIA has continuously reinvented itself over two decades. Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing. NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can address, and that matter to the world. This is our life’s work, to amplify human creativity and intelligence. Make the choice to join us today. Design-for-Test Engineering at NVIDIA works on groundbreaking innovations involving crafting creative solutions for DFT architecture, verification and post-silicon validation on some of the industry's most complex semiconductor chips. What you'll be doing: As a senior member in our team, you will work with pre-silicon and post-silicon data analytics - visualization, insights and modeling. Design and uphold sturdy data pipelines and ETL processes for the ingestion and processing of DFX Engineering data from various origins Lead engineering efforts by collaborating with cross-functional teams (execution, analytics, data science, product) to define data requirements and ensure data quality and consistency You will work on hard-to-solve problems in the Design For Test space which will involve application of algorithm design, using statistical tools to analyze and interpret complex datasets and explorations using Applied AI methods. In addition, you will help develop and deploy DFT methodologies for our next generation products using Gen AI solutions. You will also help mentor junior engineers on test designs and trade-offs including cost and quality. What we need to see: BSEE (or equivalent experience) with 5+, MSEE with 3+, or PhD wi

At NVIDIA, we push the boundaries of computing innovation. Our ASIC Verification Engineers focus on developing the world’s top SoCs and GPUs. Joining us as a Senior ASIC Verification Engineer - GPU means working on modern technology powering consumer graphics and AI applications. This position is ideal for those passionate about technology and eager to impact computing’s future. What you'll be doing: As a key member of our ASIC Verification team, you will verify the design and implementation of the industry's leading GPUs. You will be responsible for verifying the ASIC build, architecture, golden models, and micro-architecture using advanced verification methodologies such as UVM or equivalent. Understand the design and implementation of your unit/cluster/chip, define the verification scope, develop the verification infrastructure, and verify the correctness of the design. Collaborate with architects, designers, and pre- and post-silicon verification teams to accomplish your task. What we need to see: Bachelor's Degree in EE, CS, or CE or equivalent experience. 5+ years of relevant experience. Experience in verification using random stimulus along with functional coverage and assertion-based verification methodologies. Experience with design and verification tools (VCS or equivalent simulation tools, debug tools like Verdi, Indago, GDB). Expertise in System Verilog or similar HVL. Strong debugging and analytical skills. Perl and C/C++ programming language experience desirable. Strong communication skills and the ability & desire to work as a great teammate are huge pluses. Experience in crafting test bench environments for unit and system level verification. #LI-Hybrid Your base salary will be det

We are now looking for a Senior Deep Learning Software Engineer, PyTorch. NVIDIA is hiring software engineers to design and build tools used by AI engineers across the world to design, develop, and deploy AI applications scalable across thousands of GPUs. This position will embed you in an ambitious and diverse team that influences all areas of NVIDIA's AI platform as well as directly contributes to PyTorch, a premiere deep learning framework. In this role you will work with multiple teams at NVIDIA across fields, as well as collaborate internationally with the PyTorch community to develop the best AI platform in the world. What you will be doing: Design and build PyTorch components that run efficiently on supercomputers with 1000s-100ks of GPUs. Collaborate with NVIDIA’s hardware and software teams to improve the overall GPU performance in PyTorch. Design, build and support production AI solutions used by enterprise customers and partners. Work with internal applied researchers to improve their AI tools. What we need to see: BS in Computer Science or Engineering (or equivalent experience). 3+ years professional experience in deep learning. Proficient with C++ programming. Strong understanding of systems software and interfaces. Demonstrated experience with Thread and Distributed Parallel Programming Demonstrated background developing large software projects. Strong verbal and written communication skills Ways to stand out from the crowd: Contributions and participation in the open source community. Familiarity with deep learning compilers. Familiarity with deep learning modeling trends. Background with CUDA Programming as well as Python.

Everpure (NYSE: P) has evolved from storage pioneer to data platform, closing fiscal 2026 with $3.7 billion in revenue, its first billion-dollar quarter, and accelerating growth into FY27. Our strategic agenda spans the companies defining the next era of technology - hyperscalers, AI labs, the AI hardware supply chain, data platform providers, and the broader AI ecosystem. This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us. THE ROLE Everpure (formerly Pure Storage) is hiring a Senior Pre-Sales Systems Engineer to support our Higher Education business, with a focus on R1 research universities. R1 institutions are not one customer. A single campus can behave like a dozen different organizations at the same time: central IT running enterprise applications, research computing and HPC teams, genomics and imaging cores, AI and machine learning labs competing for GPU time, an academic medical center with its own rules and its own budget, and faculty groups buying infrastructure directly with grant money. Decisions move through CIOs, research computing directors, principal investigators, sponsored programs offices, procurement, and consortium contract vehicles, often in parallel and not always in agreement. That environment rewards someone who can hold a lot of context at once, translate between very different audiences, and connect technology to the outcomes these institutions actually care about: time to result for researchers, compliance with sponsor and grant requirements, data that stays usable for a decade or more, and budgets that have to stretch further every year. You will be the technical lead on these accounts, working alongside account managers, specialists, and partners to build solutions customers can defend internally, both technically and financially. Location: MA / NY / PA WHAT YOU'LL DO Develop a real understanding of what drives ea

Location: United States – Remote Clearance: Ability to obtain and maintain a Public Trust LTS is seeking a Senior AI Platform and Harness Engineer to develop and maintain the infrastructure, tooling, and evaluation frameworks that power enterprise AI solutions. This role is responsible for building the AI platform and reusable "AI harnesses" that enable Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG), and Generative AI applications to be securely developed, tested, evaluated, monitored, and deployed at scale. The ideal candidate has experience with AI platforms, LLMOps, software engineering, cloud-native technologies, and backend systems, along with a passion for building reliable, observable, and production-ready AI solutions. You will work closely with AI architects, software engineers, data scientists, and product teams to ensure AI solutions are scalable, secure, cost-effective, and continuously improving. What You'll Do: Design, build, and maintain enterprise AI platform capabilities supporting Large Language Models (LLMs), AI agents, RAG, and Generative AI applications. Lead data preparation, training and fine-tuning of LLMs Develop reusable AI harnesses to automate testing, prompt evaluation, model benchmarking, regression testing, and quality assurance. Build AI evaluation frameworks to measure model accuracy, retrieval quality, hallucination detection, latency, throughput, cost, and overall application performance. Implement observability and monitoring solutions for AI applications, including telemetry, tracing, logging, dashboards, and operational metrics. Build and maintain LLMOps pipelines supporting model deployment, versioning, evaluation, experimentation, rollback, and continuous improvement. Design automated workflows for prompt testing, retrieval evaluation, AI system validation, and performance benchmarking. Develop internal tools for prompt management, model experimentation, AI performance optimization, and developer