Cuda Full Stack And Ai Test Dev Intern Jobs

We are looking for an enthusiastic software engineer to join our AI networking acceleration team, to work on a groundbreaking open-source library, using hardware offloads, GPU Kernels and RDMA network cards. Our product is a performance-oriented low-level infrastructure, crafted to change the way inference works. We thrive as a team in a deeply strong environment, and we're passionate about innovation. The rewards are sweet and include working with some of the brightest people in the industry, an aggressive compensation plan that rewards top performers, and the opportunity to collaborate on products that transform daily the way people work and play. What you'll be doing: Developing a highly optimized inference framework Running on the world’s largest supercomputers and data centers. The work environment is dynamic and challenging as our employees work on innovative, next-generation products at the forefront of technology in terms of performance, scalability, and features. What we need to see: B.Sc. or equivalent experience in Computer Science or Software Engineering 6+ years of experience in modern C++ / C / Rust development 3 years of experience in Linux environment and familiarity with development tools Deep knowledge of the TCP/IP network stack Understanding of computer architecture and operating systems concepts Ways to stand out from the crowd: Background in Linux internals and low-level software optimizations (benchmarking, bottleneck research, performance tuning) Experience in programming CUDA kernels is an advantage Familiarity with ML frameworks and LLMs Background in parallel programming / high-performance computing / RDMA t

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 our 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 an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. This role owns the EMEA go-to-market motion for NVIDIA's Agentic AI Platform, helping customers and partners adopt, deploy, secure, and scale agentic AI on NVIDIA. The role works across enterprise, AI-native, ISV, cloud provider, public sector, defense, and regulated environments, translating NVIDIA software into practical adoption motions for priority workloads. The person should bring strong breadth across open and sovereign models, Nemotron, NVIDIA Agent Toolkit, NeMo, NIM, secure agent runtimes, evaluation harnesses, guardrails, confidential computing, cybersecurity, legal/commercial guidance, and AI Factory reference architectures What you'll be doing: Execute the EMEA Agentic AI Platform GTM plan across open models, Nemotron adoption, regulated software hardening, confidential computing, cybersecurity, and secure AI deployment. Position NVIDIA Agent Toolkit, NeMo, Nemotron, NIM, Dynamo, CUDA-X libraries, evaluation harnesses, guardrails, DGX OS, and AI Enterprise as one adoption path. Refine the sales toolkit across technical, commercial, legal, support, and terms-and-conditions tracks so field teams and partners can confidently position

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.

Ignite your curiosity. Solve the unsolvable. At Leidos, we do more than write code—we decode the unknown. Our San Diego-based research and engineering team takes on some of the nation’s toughest defense challenges using advanced signal processing, ocean remote sensing, and high-performance computing. We’re seeking a Software Engineer / Computer Scientist who enjoys solving complex problems and pushing the limits of performance. In this role, you’ll work alongside a multidisciplinary team of scientists and engineers with expertise in hydrodynamics, physics, acoustics, and signal processing to build impactful software that turns massive, complex data sets into meaningful insight. If you are motivated by innovation, energized by collaboration, and excited to see your work support real-world missions, this could be the right opportunity for you. What You’ll Do Collaborate with scientists and engineers to design, develop, and optimize advanced algorithms for next-generation radar, optical, and infrared sensor systems. Build scalable, high-performance backend systems for scientific computing in distributed environments. Integrate, refactor, and improve scientific codebases to increase efficiency and scalability. Translate and optimize existing code for GPU/CUDA acceleration and parallel or distributed execution. Test, document, maintain, and enhance complex software in Linux/Unix environments. Contribute in a collaborative environment that values technical excellence, creativity, and continuous growth. Required Qualifications Bachelor’s degree in Computer Science, Applied Mathematics, Physics, or a related field with 4+ years of backend software development experience, or a Master’s degree with 2+ years of experience. Equivalent experience may be considered in place of a degree. U.S. citizenship and the ability to obtain a Top Secret clearance; active Top Secret clea

Join our multidisciplinary team and help build and improve GPU and CPU accelerated data processing software libraries. Projects like DALI or nvImageCodec are used in all kinds of processing workflows and support NVIDIA's vision and growth. Starting from powering AI, data analytics, image processing, computer vision, and scientific simulations for leading commercial and academic organizations worldwide. In this role, you will design, develop, and optimize pioneering algorithms. Ideal candidates will have experience with accelerated computing and a passion for advancing the state-of-the-art in various computing domains. If this sounds exciting, we would love to meet you! What you’ll be doing: Developing scalable library software using modern tools and languages for various numerical method. Performance tuning, optimization, and benchmarking of algorithms on various architectures. Working closely with leadership team and other internal and external partners to understand feature and performance requirements and contribute to the technical roadmaps of libraries. Providing technical leadership and guidance to library engineers working with you. Find opportunities to improve user experience and library performance. What we need to see: PhD or MSc’s degree in Computational Science, Computer Science, Applied Math, or related science or engineering field of study is preferred (or equivalent experience). 5+ years experience developing, debugging, and optimizing high-performance parallel numerical applications on modern computing platforms, with GPU acceleration using CUDA. C/C++ programming and software development skills. Proven experience in leading and completing software development projects. Strong collaboration, communicati

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 our 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 an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. We are looking for an AI Native Startup Partner Managers to join NVIDIA for Startups — NVIDIA’s business got startups building on NVIDIA technologies across AI, accelerated data science, high-performance computing, and advanced visual computing. What you’ll be doing: Identify, engage, technically assess, and recruit the most relevant and promising startups across Europe building on NVIDIA-accelerated platforms, with a focus on Media & Entertainment, LegalTech, AI Developer Tools, Data Science, and Generative AI Evaluate startup technical maturity, architecture decisions, and platform alignment, including usage of CUDA libraries, NVIDIA SDKs, AI frameworks, GPU-accelerated data pipelines, and training/inference stacks Share ecosystem, technology, and developer-level insights with internal partners, translating startup feedback into actionable input for NVIDIA platform, SDK, and product teams Act as the voice of NVIDIA Inception members by developing a deep understanding of their technical roadmaps, research foundations, system architectures, and platform dependencies Provide AI Native priori

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. As a Principal Software Engineer on the Compute team, you will be the technical anchor for Roblox's GPU and AI accelerator capabilities. This is a battle-tested GPU expert role focused on the machine management layer and above: how GPU hosts are made production-ready, kept healthy, and turned into reliable compute for the workloads that depend on them. You will own the hard problems that show up only at scale, from driver and firmware management to GPU health, reliability, and performance across a rapidly growing fleet of accelerators spanning Roblox data centers and cloud environments. You will set the technical direction for GPU compute and up-level the entire organization's GPU expertise. You will: Serve as the GPU technical leader for the Compute team, partnering across Kubernetes, Machine Bootstrap, Networking, and Cloud to drive GPU strategy end to end. Own the GPU host lifecycle above raw fleet management: driver, firmware, and CUDA stack management, GPU health and telemetry, and remediation of GPU-specific failures (XID errors, ECC, thermal, NVLink and fabric faults). Architect how GPU capacity is exposed to compute platforms, including scheduling, isolation, and integration with Ku

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE: Baseten’s Model Performance (MP) team is responsible for ensuring the models running on our platform are fast, reliable, and cost‑efficient. As part of this team, you’ll focus on Model APIs — the infrastructure powering our hosted API endpoints for the latest open‑source models. This work spans distributed systems, model serving, and developer experience. You’ll join a small, high‑impact team operating at the intersection of product, model performance, and infra, helping to define how developers interact with AI models at scale. RESPONSIBILITIES: Design, build, and operate the Model APIs surface with focus on advanced inference capabilities: structured outputs (JSON mode, grammar-constrained generation), tool/function calling and multi-modal serving Profile and optimize TensorRT-LLM kernels, analyze CUDA kernel performance, implement custom CUDA operators, tune memory allocation patterns for maximum throughput and optimize communication patterns across multi-GPU setups Productionize performance improvements across runtimes with deep understanding of their internals: speculative decoding implementations, guided generation for structured outputs, custom scheduling and routing algorithms for high-performance serving Build comprehensive benchmarking frameworks that measure real-world performance across different model architectures, batch sizes, sequence lengths, and hardware configurations Productionize performa

Scale's LLM post-training platform team builds our internal distributed framework for large language model training. The platform powers MLEs, researchers, data scientists, and operators for fast and automatic training and evaluation of LLMs. It also serves as the underlying training framework for the data quality evaluation pipeline. Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely with Scale’s ML teams and researchers to build the foundation platform which supports all our ML research and development works. You will be building and optimizing the platform to enable our next generation LLM training, inference and data curation. If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you! You will: Build, profile and optimize our training and inference framework. Collaborate with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation. Research and integrate state-of-the-art technologies to optimize our ML system. Ideally you’d have: Passionate about system optimization Experience with multi-node LLM training and inference Experience with developing large-scale distributed ML systems Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc. Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. Strong written and verbal communication skills to operate in a cross functional team environment. Nice to haves: Demonstrated expertise in post-training methods and/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc. Compensation packages at Scale for eligible roles include base salary, equity,

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We’re seeking a GPU Kernel Engineer to join our team at the cutting edge of AI acceleration, where your code directly impacts the performance of state-of-the-art machine learning models. As a GPU Kernel Engineer, you'll craft the foundation that powers modern AI workloads, optimizing every microsecond of computation to enable breakthrough applications. You'll work in a fast-paced, intellectually stimulating environment where technical excellence is paramount and your contributions directly influence production systems serving millions of users across numerous products. This role offers exceptional growth potential for engineers passionate about low-level optimization and high-impact systems work. EXAMPLE INITIATIVES You'll get to work on these types of projects as part of our Model Performance team: Baseten Embeddings Inference: The fastest embeddings solution available The Baseten Inference Stack Driving model performance optimization RESPONSIBILITIES Core Engineering Responsibilities Design and implement high-performance GPU kernels for key ML operations, including matrix multiplications, attention mechanisms, and mixture-of-experts routing Write and optimize code using CUDA, PTX assembly, and architecture-specific techniques Apply advanced performance optimization methods such as memory coalescing, warp-level programming, tensor core acceleration, and compute/memory overlap Performance & Innovation Implem

Scale’s ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM's, as well as evaluation of data quality. Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale’s ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation. If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you! You will: Build, profile and optimize our training and inference framework Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation Research and integrate state-of-the-art technologies to optimize our ML system Ideally you’d have: Strong excitement about system optimization Experience with multi-node LLM training and inference Experience with developing large-scale distributed ML systems Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. Strong written and verbal communication skills and the ability to operate in a cross functional team environment Nice to haves: Demonstrated expertise in post-training methods &/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the positi

About the Team Our Inference team brings OpenAI’s most capable research and technology to the world through our products. We empower consumers, enterprise and developers alike to use and access our start-of-the-art AI models, allowing them to do things that they’ve never been able to before. We focus on performant and efficient model inference, as well as accelerating research progression via model inference. About the Role We are looking for an engineer who wants to take the world's largest and most capable AI models and optimize them for use in a high-volume, low-latency, and high-availability production and research environment. In this role, you will: Work alongside machine learning researchers, engineers, and product managers to bring our latest technologies into production. Work alongside researchers to enable advanced research through awesome engineering. Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our model inference stack. Build tools to give us visibility into our bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues. Optimize our code and fleet of Azure VMs to utilize every FLOP and every GB of GPU RAM of our hardware. You might thrive in this role if you: Have an understanding of modern ML architectures and an intuition for how to optimize their performance, particularly for inference. Own problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done. Have at least 5 years of professional software engineering experience. Have or can quickly gain familiarity with PyTorch, NVidia GPUs and the software stacks that optimize them (e.g. NCCL, CUDA), as well as HPC technologies such as InfiniBand, MPI, NVLink, etc. Have experience architecting, building, observing, and debugging production distributed systems. Bonus point if worked on performance-critical distributed systems. Have need

About the Role Together AI is seeking a Machine Learning Engineer to join our Inference Engine team, focusing on optimizing and enhancing the performance of our AI inference systems. This role involves working with state-of-the-art large language models models and ensuring they run efficiently and effectively at scale. If you are passionate about AI inference, PyTorch, and developing high-performance systems, we want to hear from you. This position offers the chance to collaborate closely with AI researchers and engineers to create cutting-edge AI solutions. Join us in shaping the future at Together AI! Responsibilities Design and build the production systems that power the Together AI inference engine, enabling reliability and performance at scale. Develop and optimize runtime inference services for large-scale AI applications. Collaborate with researchers, engineers, product managers, and designers to bring new features and research capabilities to the world. Conduct design and code reviews to ensure high standards of quality. Create services, tools, and developer documentation to support the inference engine. Implement robust and fault-tolerant systems for data ingestion and processing. Requirements 3+ years of experience writing high-performance, well-tested, production-quality code. Proficiency with Python and PyTorch. Demonstrated experience in building high performance libraries and tooling. Excellent understanding of low-level operating systems concepts including multi-threading, memory management, networking, storage, performance, and scale. Preferred: Knowledge of existing AI inference systems such as TGI, vLLM, TensorRT-LLM, Optimum Preferred: Knowledge of AI inference techniques such as speculative decoding. Preferred: Knowledge of CUDA/Triton programming. Nice to have: Knowledge of Rust, Cython and compilers. About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference th

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g., Seaborn , Luigi ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for strong engineers with experience in making ML systems performant at scale. If you are interested in contributing to open-source projects and Modal’s container runtime to push language and diffusion models towards higher throughput and lower latency, we’d love to hear from you! Requirements: 5+ years of experience writing high-quality, high-performance code. Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT). Familiarity with Nvidia GPU architecture and CUDA. Experience with ML performance engineering (tell us a story about boosting GPU performance — debugging SM occupancy issues, rewriting an algorithm to be compute-bound, eliminating host overhead, etc). Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).

Role Overview As a Systems Research Engineer Intern specialized in GPU Programming, you will play a crucial role in developing and optimizing GPU-accelerated kernels and algorithms for ML/AI applications. Working closely with the modeling and algorithm team, you will co-design GPU kernels and model architecture to enhance the performance and efficiency of our AI systems. Collaborating with the hardware and software teams, you will contribute to the co-design of efficient GPU architectures and programming models, leveraging your expertise in GPU programming and parallel computing. Your research skills will be vital in staying up-to-date with the latest advancements in GPU programming techniques, ensuring that our AI infrastructure remains at the forefront of innovation. This internship is based on-site at our San Francisco HQ, running through the Winter term from January to April. Responsibilities Optimize and fine-tune GPU code to achieve better performance and scalability Collaborate with cross-functional teams to integrate GPU-accelerated solutions into existing software systems Stay up-to-date with the latest advancements in GPU programming techniques and technologies Requirements Strong background in GPU programming and parallel computing, such as CUDA and/or Triton. Knowledge of ML/AI applications and models Knowledge of performance profiling and optimization tools for GPU programming Excellent problem-solving and analytical skills About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Internship Program Details:&n