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Senior Solutions Architect at NVIDIA designing AI-augmented engineering workflows for industrial simulation platforms, accelerating solvers and digital twins using GPUs, Omniverse, and physics-ML.
Design and deploy high-performance storage systems for AI/HPC workloads, automate infrastructure, and optimize distributed storage solutions across on-prem and cloud environments.
Administers and troubleshoots high-performance computing clusters running Windows Server and Red Hat Linux, ensuring uptime and security compliance for defense systems.
Engineer scalable compilation and synthesis flows for NVIDIA’s hardware emulation platforms, using EDA tools like Synopsys and Cadence to validate complex chip designs before fabrication.
Designs and advises on scalable AI cloud solutions for enterprise customers, optimizing distributed training and inference pipelines using PyTorch, Kubernetes and other ML tooling.
Build and own a shared platform for benchmarking and evaluating scientific software and AI agents using C++, Python, CUDA, and PyTorch across large GPU fleets.
Develop and deploy deep learning models for industrial robotics, training vision and decision systems for tasks like welding and coating in real factory settings.
Builds backend services and data pipelines in Python to orchestrate AI-driven scientific workflows, containerized environments, and observability tools for a deeptech startup in Berlin.
You design, implement, maintain, and support high-performance compute and storage systems. You build software and operating system tooling, monitor performance and faults, collaborate on code and testing…
The role: Build the control plane and data plane for physical AI. Munari must run training, simulation, evaluation, data processing, and inference workloads across public cloud, customer-owned infrastructure, robotics…
Builds and optimizes large-scale AI infrastructure, including Kubernetes clusters, RDMA networking, and GPU orchestration to improve efficiency and scalability of AI training/inference systems.
SECURITY CLEARANCE: TS/SCI with both Polygraphs is required POSITION: Expert HPC Network/Systems Engineer REQUISITION: K EY-TE.HPC-03.080626 LOCATION : Ft. Meade, Maryland JOB DESCRIPTION: • (U) Provide Tier 4 HPC…
Position Overview: As a Reliability Software Engineer in the Risk team, you will play a critical role in ensuring the performance, stability and availability of the Risk software systems, as well as their day-to-day…
Research and build power-aware AI infrastructure systems that scale data centers sustainably using distributed computing and optimization techniques.
Build and optimize AI networking frameworks and benchmarks for NVIDIA’s supercomputers and data centers, using Python, Linux, and modern AI tools like PyTorch.
Build and scale deep-learning infrastructure for autonomous-vehicle training, optimizing multi-thousand-GPU clusters, distributed training, and massive video datasets.
Build and optimize distributed GPU training infrastructure for large neural networks, focusing on PyTorch, Megatron-LM, and DeepSpeed to maximize throughput and stability at scale.
Architect and build a managed AI cloud platform from scratch, including GPU provisioning, Kubernetes/Slurm orchestration, and high-performance storage, to deliver scalable, power-flexible AI infrastructure.
Lead the design of next-gen AI infrastructure at Crusoe, unifying bare-metal, virtualized, and containerized compute while optimizing kernel-level I/O for massive-scale LLM training.
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