Artificial Intelligence Developer in United States

We are hiring senior engineers to work on the CUDA driver, a core component of our platform for accelerating general purpose computation on the GPU. Our team delivers features and improvements to better realize the potential of NVIDIA hardware for a growing range of computational workloads, ranging from deep learning, scientific computation, and self-driving cars to video games and virtual reality! CUDA defines a unified programming model across a range of system configurations and hardware capabilities. To accomplish this, the CUDA driver interacts with GPU hardware, kernel mode drivers, switches and the operating system. What you'll be doing: As a member of our team, you will use your design abilities, coding expertise, and creativity to deliver the best Compute platform in the world. You will craft elegant solutions to exciting problems and craft the future direction of CUDA as you collaborate with your peers across NVIDIA. You will evangelize, architect, and implement new CUDA features You'll oversee and drive development efforts across multiple teams Collaborate with members of hardware architecture teams Help define forward-looking improvements to the CUDA APIs and programming model Design and maintain performance and precision modeling Write effective, maintainable, and well-tested code Develop code for multiple operating systems What we need to see: Bachelor of Science or Master of Science degree in Computer Science, Electrical Engineering, or related field (or equivalent experience) 15&#43; years of relevant systems software development experience Strong C programming skills </

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Key Responsibilities Overlay Strategy, Roadmap & Technical Leadership Own the overlay roadmap including bond overlay technologies. Serve as the technical authority for overlay, defining overlay specifications, error budgets, mark strategies, technical signoff criteria, and scaling requirements for future technology nodes. Develop innovative overlay solutions that enable advanced bonding while meeting performance, yield, manufacturability, and scaling objectives. Drive advancement of overlay metrology, automation, process control, modeling, and analytics capabilities in partnership with Process and Metrology teams. Apply advanced analytics, Artificial Intelligence, simulation, and experimental methodologies to accelerate learning, identify root causes, and improve overlay performance. Technology Development & Problem Solving Lead simulation and experimental process development activities, balancing modeling with hands-on characterization and technology development. Develop and maintain overlay error budgets, validation plans, experiments, and technical assessments that connect process behavior to device and integration requirements. Solve complex overlay challenges involving alignment, distortion, deformation, process interactions, and bonded wafers. Translate technical findings into actionable process improvements, technology decisions, and roadmap recommendations. <

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose. We are accepting applications for this position until 10/16/2026 Position Overview: Freddie Mac Multifamily is a dynamic organization that provides interns with meaningful opportunities to build both technical and professional skills while expanding their network across the business. As a Digital Product Analysis Intern, you will work alongside product, business, and technology teams to help deliver innovative digital capabilities that support Multifamily business objectives. This role is ideal for students interested in product management, business analysis, Agile delivery, and technology-driven solutions. You will gain hands-on experience translating business needs into product requirements, facilitating collaboration across teams, and supporting digital product development initiatives that create value for customers and business stakeholders. Our Impact: The Multifamily Digital Products team serves as a bridge between business needs and technology solutions. We work closely with business stakeholders, product owners, and development teams to identify opportunities, improve processes, and deliver innovative digital products that enhance operational efficiency and business outcomes. Our team leverages Agile methodologies, data-driven decision-making, and emerging technologies—including AI—to continuously improve product quality and user experiences. Your Impact: Support a Multifamily Digital Product team by serving as a junior Product Analyst and contributing to Agile product delivery activities. Facilitate commun

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose. We are accepting applications for this position until 10/16/2026 Position Overview: At Freddie Mac, you will do meaningful work that helps build a stronger housing finance system and supports homeownership and rental housing opportunities across the nation. Launch your career by contributing to one of the most important risk management functions in the financial services industry. Our internships and graduate programs provide opportunities to tackle complex business challenges, collaborate with experienced professionals, and make an impact from day one. As an Enterprise Risk Business Intern, you will gain exposure to enterprise-wide risk management practices, emerging technology risks, AI governance, and strategic oversight activities while developing valuable analytical, communication, and leadership skills. Our Impact: Enterprise Risk Management (ERM) helps build and maintain a strong, effective, and efficient risk management framework across Freddie Mac. We provide independent oversight and assessment of financial and non-financial risks while promoting a strong risk culture throughout the organization. As risk management continues to evolve, our teams play an increasingly important role in overseeing areas such as artificial intelligence, model risk, fraud risk, data governance, and enterprise-wide risk frameworks. We work across business functions to ensure that risk management practices remain effective, scalable, and aligned with regulatory and busines

About the Team Our Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve peoples’ lives. About the Role We are seeking an Electrical Engineer to build and own the electrical backbone of our robotic actuator dynamometer and test infrastructure. You will design, integrate, and operate the load motor drives, power distribution, instrumentation wiring, DAQ interfaces, and safety systems that make high-performance robotic actuator testing repeatable, safe, and scalable. This role spans hands-on lab execution and system architecture: selecting and commissioning power electronics, designing robust test-cell electrical systems, bringing up sensors and DAQ, and partnering with mechanical and software engineers to turn robotic actuator hardware into trustworthy data. In this role, you will Own the electrical architecture of dynamometer and actuator test cells, from mains distribution and protection through load motor drives, braking, and auxiliary power. Specify, integrate, commission, and tune motor drives and load machines for robotic actuator torque, speed, efficiency, thermal, and durability testing. Design power distribution, grounding, shielding, cable routing, and connectorization for high-current, high-voltage, and low-level measurement systems. Integrate torque, position, speed, temperature, voltage, current, vibration, and other instrumentation from robotic actuators into DAQ and control systems. Develop electrical schematics, wiring diagrams, panel layouts, harness documentation, and test-cell interface definitions. Build, debug, and maintain test-cell electrical hardware, rapidly diagnosing noise, EMI, grounding, drive, sensor, and power-q

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We’re looking for a Rack Power Engineer with deep expertise in high-power conversion and distribution to design, qualify, and support power systems for AI supercomputers. You will own rack power solutions—including power shelves, AC/DC rectifiers, power supply units (PSUs), power management controllers (PMCs), and high-current distribution—from requirements and supplier development through deployment. You will also monitor fleet rack power health, lead debugging and root-cause investigations, and drive improvements into hardware, firmware, and qualification coverage. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Own rack power architecture and requirements for high-power AI supercomputing systems, including power budgets, AC input interfaces, DC distribution, redundancy, efficiency, serviceability, and integration with data center infrastructure. Drive the design and supplier development of power shelves, rectifiers, PSUs, PMCs, busbars, connectors, and protection circuits. Review electrical designs and control behavior, and evaluate performance, cost, reliability, and availability trade-offs. Define and execute component, shelf, and rack qualification plans covering load transients, current sharing, hot-swap, startup and shutdown, redundancy failover, fault protection and recovery, thermal limits, and AC disturbances and ride-through

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. We are looking for a motivated Deep Learning engineer to bring advanced communication technologies into AI stacks, including PyTorch, TRT-LLM, vLLM, SGLang, JAX, etc. You will be working with the team that created communication libraries like NCCL, NVSHMEM & technology like GPUDirect -- for scaling Deep Learning and HPC applications. Your customers will have diverse multi-GPU demands, ranging from training on scales up to 100K GPUs to inference down at microsecond latency. Communication performance between the GPUs has a direct impact on AI applications. Your work in AI toolkits will make all of those easier for the community. This is an outstanding opportunity for someone with an AI background to advance the state of the art in this space. Are you ready to contribute to the development of innovative technologies and help realize NVIDIA's vision? What you will be doing: Integrate new communication libraries features in AI frameworks: from PoC to performance analysis to production Perform deep analysis of AI workloads and frameworks to identify multi-GPU communication requirements and opportunities. Collaborate hands-on with teams working on the latest AI models. Improve AI compilers to hide communications or perform automatic fusion. Conduct in-depth AI workload performance characterization on multi-GPU clusters. Design fault-tolerant and elastic solutions for large-scale or dynamic AI workloads. Author

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA is looking for phenomenal people like you to help us accelerate the next wave of artificial intelligence. We are looking for a highly motivated senior software engineer for an exciting role in our communication libraries and network software team. The position will be part of a fast-paced crew that develops and maintains software for complex heterogeneous computing systems that power disruptive products in High Performance Computing and Deep Learning. What you will be doing: Design, implement and maintain highly-optimized communication runtimes for Deep Learning frameworks (e.g. NCCL for TensorFlow/Pytorch) and HPC programming interfaces (e.g. UCX for MPI/OpenSHMEM) on GPU clusters. Participating in and contributing to parallel programming interface specifications like MPI/OpenSHMEM. Design, implement and maintain system software that enables interactions among GPUs and interactions between GPUs and other system components. Creating proof-of-concepts to evaluate and motivate extensions in programming models, new designs in runtimes and new features in hardware. What we need to see: M.S./Ph.D. degree in CS/CE or equivalent experience. 5&#43; years of relevant experience. Excellent C/C&#43;&#43; programming and debugging skills. Strong experience with Linux. Expert understanding of computer syst

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. We are the GPU Communications Libraries and Networking team at NVIDIA. We deliver libraries like NCCL, NVSHMEM, UCX for Deep Learning and HPC. We are looking for a motivated Performance engineer to influence the roadmap of our communication libraries. The DL and HPC applications of today have a huge compute demand and run on scales which go up to tens of thousands of GPUs. The GPUs are connected with high-speed interconnects (eg. NVLink, PCIe) within a node and with high-speed networking (eg. Infiniband, Ethernet) across the nodes. Communication performance between the GPUs has a direct impact on the end-to-end application performance; and the stakes are even higher at huge scales! This is an outstanding opportunity for someone with HPC and performance background to advance the state of the art in this space. Are you ready for to contribute to the development of innovative technologies and help realize NVIDIA's vision? What you will be doing: Conduct in-depth performance characterization and analysis on large multi-GPU and multi-node clusters. Study the interaction of our libraries with all HW (GPU, CPU, Networking) and SW components in the stack Evaluate proof-of-concepts, conduct trade-off analysis when multiple solutions are available Triage and root-cause performance issues reported by our customers Collect a lot of performance data; build tools and infrastructure to visualize and analyze the information <li

NVIDIA is seeking a Senior Software Engineer to help us develop distributed storage services for AI/ML. In this role you will work closely with the broader NVIDIA team to design and build a reliable, scalable, and efficient storage-as-a-service tailored to AI applications that can be deployed anywhere and scale without limitations. This service supports the whole NVIDIA critical business from graphics drivers to autonomous vehicles to deep learning frameworks. To achieve this goal, we are looking for an engineer with a deep understanding of distributed systems, outstanding design skills, and a track record in building and delivering large-scale distributed services. What you will be doing: Leading the overall architecture and design of our distributed storage service optimized for AI/ML Develop and maintain distributed, robust and scalable Go programs deployed to state of the art open-source ecosystems, including Kubernetes. Develop and maintain user-space applications, containers, Go-bindings, and CLI tools. Building features for a distributed storage service to enhance availability and reliability for large-scale deployments Engaging and collaborating with NVIDIA Research, Computing, Product teams, cross-functional teams, and external customers to deliver Cloud services. Automating distributed storage service end-to-end, including deployment, management, and monitoring What we need to see: Bachelor’s of Science in Computer Science, or related field (or equivalent experience) with 8&#43; years of industry experience Strong background in developing distributed systems involving Golang, Kubernetes, and Cloud Service Provider integrations Strong track record of delivering distributed services in a variety of distributed computing environments Experience in i

NVIDIA is a leading artificial intelligence computing company, and we are paving the way with innovations in self-driving cars, machine learning, supercomputing, gaming, and visualization. We give automakers, tier-1 suppliers, automotive research institutions, and start-ups the power and flexibility to develop and deploy breakthrough artificial intelligence systems for self-driving vehicles. Our unified computing architecture enables training deep neural networks in the data center, and then seamlessly runs them on NVIDIA DRIVE Platforms inside the vehicle. The Hypervisor and RTOS Team within NVIDIA DRIVE Software plays a critical role in NVIDIA's expansion into the world of artificial intelligence and autonomous vehicles. Our job is to facilitate the sharing and separation of system resources while achieving real-time, safety, and security requirements. We develop Hypervisor and RTOS with a strong focus on automotive quality, safety and security needed for the real-time, highly available system level components of world-class Autonomous Vehicles. We are making extensive use of formal methods to automate our workflow and increase the quality of our SW. We are hiring now for the position of Senior System Software Engineer for Hypervisor and RTOS What you’ll be doing: Design and develop new features for RTOS and hypervisor software stack. Bring up and optimize RTOS and hypervisor stacks on new NVIDIA Tegra SoCs. Develop high-integrity software using best-in-class engineering, safety, and security practices. Debug complex system-level issues across hardware, firmware, RTOS, and virtualization layers. Lead team-wide technical initiatives by building alignment, coordinating execution, and driving them to completion. What we need to see: BS, MS in CS/CE/EE or a related engineering field or equivalent experience </

We are developing advanced multi-rack, multi-tenant AI/ML datacenters with NVIDIA GB200, and upcoming GB300 GPUs. NVIDIA seeks a Senior Software Engineer for our CSP (Cloud Service Provider) Engagements team to focus on the cloud-native stack for datacenter products like GB200. In this role, You will define customer workflows, prototype stack enhancements, and debug the toughest Kubernetes &#43; Slurm issues in multi-rack, multi-tenant AI datacenters. You'll tackle complex scheduling challenges across racks, tenants, and clouds as part of the CSP engagements team. What you’ll be doing: Perform deep-dive debugging of multi-rack, multi-tenant clusters: scheduler behavior, container runtime issues, device-plugin crashes, RDMA/IB fabric anomalies, etc. Gather customer requirements and prototype feature extensions for Kubernetes operators, Slurm plugins, and custom micro-services that expose new GPU capabilities. Drive joint architecture reviews and “whiteboard” sessions with CSP and internal platform teams; convert findings into RFCs and upstream pull requests. Create reproducible testbeds (Helm/Ansible/Terraform) that mirror customer environments; automate validation and benchmark suites. Deliver technical collateral-design docs, how-to guides, demo scripts-and present at customer on-sites, KubeCon, and SlurmUG. Collaborate with AE, FAE, and Solution Architect teams to deliver integrated customer solutions and technical documentation. What we need to see: Strong source-level expertise in Kubernetes internals (scheduler, CRI/CNI/CSI, operators) and Slurm (federation, power-save, plugins). Hands-on experience integrating next-gen GPUs (Blackwell/GB200/GB300) or comparable accelerators into containerized clusters. Proven track record debugging large-scale, cloud-native stacks across ne

About Graphcore Graphcore is a global leader in artificial intelligence computing systems. We design advanced semiconductors and data center hardware that provide the specialized processing power needed to advance AI while improving the efficiency required for broad adoption. As part of SoftBank Group, Graphcore belongs to a family of companies developing transformative technologies. Our AI Engineering Campus in Austin plays an important role in building the hardware platforms that support the next generation of AI systems. The Opportunity We are looking for a recent graduate or early-career engineer to join the Hardware Platform Development team as a Graduate Systems Engineer. You will contribute to the design, integration, validation, and performance analysis of advanced AI compute platforms. You will work with experienced hardware, firmware, software, mechanical, thermal, and systems engineers throughout the development lifecycle. The role combines subsystem engineering, hands-on laboratory work, test automation, data analysis, troubleshooting, and clear technical documentation. Start: September, 2027 Location: Austin, Texas, USA What You Will Do Contribute to the design, integration, and testing of CPU and high-speed input and output subsystems for advanced compute platforms. Take ownership of defined engineering tasks from requirements and test planning through execution, analysis, and technical review. Develop system-level validation plans, procedures, scripts, and tools that improve test coverage, repeatability, data collection, and analysis. Evaluate platform performance, power, signal behavior, reliability, and interoperability using laboratory measurements and system data. Investigate emerging input and output technologies, including PCIe 6.0 and 800G Ethernet, and assess their use in advanced computing systems. Support platform power, cooling, and energy-efficiency investigations, including liquid-cooling systems for high-performance processor

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&#43; years professional experience in deep learning. Proficient with C&#43;&#43; 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.

About us Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone. Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation. Job Summary We are seeking a Staff Hardware Engineer to provide advanced operational, diagnostic, and engineering support for Graphcore’s Arm-based hardware platforms across lab and data center environments. This role focuses on supporting hardware bring-up, validation, and troubleshooting of complex AI compute platforms, including server blades, racks, and rack-scale infrastructure. The successful candidate will collaborate closely with engineering, platform, and data center teams to ensure the reliability and performance of next-generation AI systems. The Team The Systems Engineering and Hardware Engineering teams are responsible for enabling the bring-up, validation, and operational reliability of Graphcore’s AI infrastructure platforms. The team works closely with server engineering, firmware teams, platform architects, and data center operations to support the development, testing, and deployment of next-generation AI compute systems. This collaborative environment enables rapid problem-solving and continuous improvement of Graphcore’s hardware platforms from early development through production deployment.