VP, AI Product Platform for Frontier AI

Coreweave; Washington (NJ); Hybrid; USD 233,000 - 341,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) with employer match

ESPP

Tuition Reimbursement

Flexible PTO

Catered lunch at offices

Job summary

CoreWeave is seeking a visionary VP, Product AI/ML to own the product strategy and engineering for the Research Training Stack, uniting HPC and cloud-native capabilities. You will bridge the gap between the hardware and researchers, delivering a high-performance environment where frontier models are trained and iterated.

Responsibilities include shaping the product roadmap for frontier orchestration (SUNK), developing next-gen training tools, and enabling efficient RL/RLHF workflows, while

Qualifications

• 15+ years of engineering leadership experience.
• 5+ years managing large-scale infrastructure.
• Deep knowledge of Slurm, Kubernetes, and InfiniBand/RDMA.
• Experience supporting frontier model research.
• Proven ability to scale services on multi-thousand GPU clusters (H100/Blackwell/Rubin).
• Strong strategic vision and roadmapping for AI stack evolution.

Responsibilities

• Lead Frontier Orchestration and advance SUNK (Slurm on Kubernetes).
• Steer holistic training services and automated evaluation frameworks.
• Build post-training RL and RLHF pipelines for labs to refine models.
• Partner with researchers to translate requirements into product roadmaps.

Skills

Engineering leadership

Large-scale infra

Frontier model research

Strategic vision

GPU clusters

Tools

Slurm

Kubernetes

InfiniBand/RDMA

Job description

CoreWeave is seeking a visionary VP, Product AI/ML to own the product strategy and engineering for the Research Training Stack, uniting HPC and cloud-native capabilities. You will bridge the gap between the hardware and researchers, delivering a high-performance environment where frontier models are trained and iterated.

Responsibilities include shaping the product roadmap for frontier orchestration (SUNK), developing next-gen training tools, and enabling efficient RL/RLHF workflows, while