Build and standardize AI-powered coding tools, agents, and dev environments to accelerate internal software development while maintaining security and quality. Requires experience with productivity tooling for large codebases, container/CI platforms, and AI model APIs.
Ensure reliability of large GPU supercomputing clusters by diagnosing hardware/firmware/OS issues, automating monitoring, driving firmware rollouts, and working directly with vendors.
Own and debug multi-thousand-GPU network fabric (RDMA/RoCE, NVLink/NVSwitch) for large-scale AI training and inference. Requires backend language proficiency, large-scale cluster experience, and cross-stack ownership.
Site Reliability Engineer drives end-to-end reliability for AI fine-tuning platform Tinker, including SLOs, monitoring, incident response, and multi-tenant GPU scheduling. Requires distributed systems experience, software proficiency for reliability, and production incident handling.
Designs and optimizes distributed training systems scaling across thousands of GPUs for large AI models. Requires strong systems engineering, PyTorch/JAX expertise, and collaborative mindset to boost research productivity.
Designs and optimizes infrastructure for scalable reinforcement learning training of large models, improving reliability, observability, and throughput. Collaborates with researchers to productionize RL algorithms; requires strong engineering skills and deep learning framework knowledge.
Designs and optimizes distributed training infrastructure for large-scale LLMs, focusing on low-precision numerics, kernel optimizations, and communication frameworks to enable stable, scalable trillion-parameter model training. Requires strong systems engineering, deep learning frameworks knowledge, and collaborative research mindset.
Designs and optimizes high-performance ML kernels (CUDA, CuTe, Triton) for large-scale LLM training, focusing on GPU efficiency, low-precision formats, and distributed compute. Collaborates with researchers to bridge algorithms and hardware.
Designs, optimizes, and scales infrastructure for high-performance AI model inference, focusing on latency, throughput, efficiency, and reliability. Collaborates with researchers to enable production deployment of large-scale models using deep learning frameworks and distributed systems.
Designs, builds, and operates GPU supercomputing environments for large-scale AI training and inference. Automates cluster management, extends orchestration systems, and optimizes performance metrics in collaboration with researchers.
Builds and scales core infrastructure for AI model training, data systems, and developer tools in a high-impact team. Requires backend proficiency (Python/Rust), experience with large-scale clusters like Kubernetes, and end-to-end project ownership.
Build and standardize AI-powered coding tools, agents, and dev environments to accelerate internal software development while maintaining security and quality. Requires experience with productivity tooling for large codebases, container/CI platforms, and AI model APIs.
Ensure reliability of large GPU supercomputing clusters by diagnosing hardware/firmware/OS issues, automating monitoring, driving firmware rollouts, and working directly with vendors.
Own and debug multi-thousand-GPU network fabric (RDMA/RoCE, NVLink/NVSwitch) for large-scale AI training and inference. Requires backend language proficiency, large-scale cluster experience, and cross-stack ownership.
Site Reliability Engineer drives end-to-end reliability for AI fine-tuning platform Tinker, including SLOs, monitoring, incident response, and multi-tenant GPU scheduling. Requires distributed systems experience, software proficiency for reliability, and production incident handling.
Designs and optimizes distributed training systems scaling across thousands of GPUs for large AI models. Requires strong systems engineering, PyTorch/JAX expertise, and collaborative mindset to boost research productivity.
Designs and optimizes infrastructure for scalable reinforcement learning training of large models, improving reliability, observability, and throughput. Collaborates with researchers to productionize RL algorithms; requires strong engineering skills and deep learning framework knowledge.
Designs and optimizes distributed training infrastructure for large-scale LLMs, focusing on low-precision numerics, kernel optimizations, and communication frameworks to enable stable, scalable trillion-parameter model training. Requires strong systems engineering, deep learning frameworks knowledge, and collaborative research mindset.
Designs and optimizes high-performance ML kernels (CUDA, CuTe, Triton) for large-scale LLM training, focusing on GPU efficiency, low-precision formats, and distributed compute. Collaborates with researchers to bridge algorithms and hardware.
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Designs, optimizes, and scales infrastructure for high-performance AI model inference, focusing on latency, throughput, efficiency, and reliability. Collaborates with researchers to enable production deployment of large-scale models using deep learning frameworks and distributed systems.
Designs, builds, and operates GPU supercomputing environments for large-scale AI training and inference. Automates cluster management, extends orchestration systems, and optimizes performance metrics in collaboration with researchers.
Builds and scales core infrastructure for AI model training, data systems, and developer tools in a high-impact team. Requires backend proficiency (Python/Rust), experience with large-scale clusters like Kubernetes, and end-to-end project ownership.