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The role involves optimizing and maintaining the software stack for large-scale AI training, focusing on CUDA/ROCm, JAX, and PyTorch to ensure efficiency and stability in distributed training systems.

Lead end-to-end growth marketing for an AI infrastructure startup: own demand generation, paid acquisition, brand and narrative, content and community, PR and executive visibility, partner marketing, events, and measurement. Build the marketing function, select tooling, manage agencies, and translate campaigns into activation and qualified pipeline.

Design, deploy, and operate the complete networking stack for large GPU clusters: RDMA fabrics (InfiniBand/RoCE), frontend/storage networks, security, cross-site/cloud connectivity, automation, telemetry, and performance debugging to ensure the fabric never bottlenecks training and inference workloads.

Own physical health and infrastructure of GPU clusters: respond to hardware outages, monitor GPU thermals/power, coordinate vendor RMAs and data center work, rack and bring up nodes, maintain Linux OS across bare-metal servers, secure networking and SSH, and manage directory and network storage services to keep compute environment stable for research and product teams.

Owner of the GPU cluster software stack: build/version OS images, drivers, and provisioning pipelines; automate validation, upgrades, and self-healing; operate GPU-enabled Kubernetes and Slurm; maintain driver/runtime compatibility and tuned PyTorch/JAX environments; debug deep accelerator and networking issues and run observability/benchmarking tooling.

Lead full-cycle technical recruiting for software, infrastructure, and AI roles. Source, screen, and manage interviews and offers; partner with hiring managers to define roles and processes; build talent pipelines; track recruiting metrics and optimize ATS-driven workflows to scale early-stage hiring.

Develop the core systems for multimodal AI models, focusing on building model serving platforms and enhancing developer experience with tools and APIs.

The GPU Kernel Engineer will design and optimize GPU kernels for large-scale AI systems, integrating them into ML frameworks and collaborating across teams to enhance performance.

Develop the model serving platform for multimodal AI models, optimizing performance, collaborating with researchers, and ensuring reliable production infrastructure.

As a Research Engineer, you'll work on pretraining, scaling, post-training, and deployment of AI models, improving infrastructure and performance.