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Field
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(HPC) or GPU-based workloads preferred Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch). Strong proficiency with containerization (Docker), orchestration (Kubernetes), and
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following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high-performance computing (HPC
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, including Large Language Models (LLMs), agent-based systems, and Retrieval-Augmented Generation (RAG). Practical expertise in training and optimizing neural networks on high-performance (GPU-enabled
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inference Develop distributed model training and inference architectures leveraging GPU-based compute resources Implement server-less and containerized solutions using Docker, Kubernetes, and cloud-native
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megawatts. To transfer energy efficiently from the grid to CPUs/GPUs, higher system voltages are required in data centres/computer racks, and efficient power electronics converter systems based on SSTs
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networking technologies such as InfiniBand. Working knowledge of GPU technologies like CUDA and OpenCL. Experience with distributed computing job schedulers (e.g., Slurm, PBS). Familiarity with
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these resources through a cloud-native Kubernetes environment integrating large-scale CPU and GPU resources, Ceph object storage, BinderHub, Coffea-Casa, Dask, and ServiceX. This platform supports more than 500
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well as experience working with large biological datasets in scalable GPU-based computing environments. What we provide: A competitive compensation package, with comprehensive health and welfare benefits. A supportive
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 3 months ago
directly with internal and external stakeholders to resolve issues and drive operational improvements. PREFERRED: Familiarity with IT service management frameworks, vendor coordination, and GPU or cloud
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 3 months ago
supporting end-users in technical research environments. PREFERRED: Familiarity with GPU computing; scripting in Python or Bash; and container orchestration tools like Docker and Kubernetes; and experience in