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Cloud • Information Technology • Machine Learning
Design and implement distributed, exabyte-scale storage systems for AI workloads, including S3-compatible object storage and dedicated storage clusters. Improve storage reliability, durability, security, observability, throughput, latency, and resilience using technologies such as RDMA, GPU Direct Storage, and distributed filesystems. Monitor and troubleshoot production systems, develop performance dashboards, analyze telemetry, collaborate across infrastructure teams, and mentor engineers.
Cloud • Information Technology • Machine Learning
Design, build, and operate secure sandboxed runtime environments for Kubernetes and GPU-accelerated workloads. Develop GPU-aware scheduling, isolation, and resource management strategies; optimize container, VM, I/O, and GPU performance; conduct profiling and benchmarking; and contribute to Linux, container runtime, virtualization, and GPU driver architecture. Collaborate with security, platform, and infrastructure teams to establish runtime isolation and performance standards for multi-tenant systems.
Cloud • Information Technology • Machine Learning
Own the roadmap and execution for W&B Models, an experiment-tracking platform for AI researchers. Improve experiment organization, training visualization, monitoring, evaluations, and large-scale analysis across web and mobile. Conduct user research, translate ML workflows into product decisions, collaborate with engineering and design, and use data and feedback to launch polished, high-performing experiences. The role requires deep familiarity with ML research, technical product management, data visualization, AI tools, and developer or researcher audiences.
