Backed by Accel, Bessemer and NEA.
About the role
Build a self-serve compute platform. Design and own the systems that let inference engineers and researchers launch training jobs and operate inference services without managing GPU provisioning, cluster configuration, or provider-specific infrastructure. Operate the GPU fleet . Own provisioning, lifecycle management, reliability, and capacity integration across providers, giving teams a consistent way to use compute regardless of where it runs. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Inference serving stacks: vLLM, SGLang, or TensorRT-LLM
- Slurm or other HPC schedulers
- GPU kernel work in CUDA or Triton — not required, but notable
- High-speed interconnects: InfiniBand, RoCE, or RDMA in production
- Observability for ML workloads: Prometheus, Grafana, or Weights & Biases
More about this role
Perplexity serves hundreds of millions of queries a month, and every one of them fans out into multiple AI inference requests running in real time. Behind that sits a large GPU fleet spread across several cloud providers. Today, our inference engineers and researchers build models while also managing networking, securing capacity, and operating the underlying GPU clusters, responsibilities we want a dedicated platform team to own. Your job is to take ownership of that infrastructure and hide its complexity behind a unified, self-serve platform for running training and inference workloads.
Build a self-serve compute platform. Design and own the systems that let inference engineers and researchers launch training jobs and operate inference services without managing GPU provisioning, cluster configuration, or provider-specific infrastructure.
Operate the GPU fleet . Own provisioning, lifecycle management, reliability, and capacity integration across providers, giving teams a consistent way to use compute regardless of where it runs.
Solve for GPU scarcity. Build the scheduling and placement logic that finds available capacity across providers, packs it efficiently, and gets the right...
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