About the job
As a Global Capacity Lead at Baseten, you will lead the "engine room" of the company, architecting, securing, and optimizing the global GPU fleet that powers our customers' AI workloads. You’ll own the end-to-end journey of capacity management, from securing multi-million dollar GPU clusters to building the automation that ensures 99.9% uptime across multi-cloud environments.
This role is a great fit for entrepreneurial engineers who want to bridge the gap between high-finance asset management and deep infrastructure engineering. You will act as the fleet orchestrator for the world's most advanced chips, ensuring Baseten never experiences a capacity outage while maintaining elite unit economics.
To be clear, this is a high-stakes engineering role. You will be hands-on with Kubernetes orchestration while also leading specialized pods focused on the next generation of hardware, like NVIDIA’s Blackwell (B200) architecture.
Responsibilities
Lead Specialized Pods: Act as the lead for specific GPU pods (e.g., H100 or B200), managing the full lifecycle of acquisition, air traffic control, and maintenance for those assets.
Advanced Orchestration: Execute complex workload migrations and "sticky" deployment drains, ensuring deployment scheduling rules meet strict regional and compliance requirements.
Build for Scalability: Design and implement the "next version" of Baseten’s capacity management system to handle a 10x increase in GPU volume.
Financial Modeling: Leverage your understanding of unit economics to build ROI models for GPU spend, ensuring Baseten scales profitably.
Cross-Team Collaboration: Partner with SRE, Infra, and FDE teams to take discrete operational tasks off their plate and verify "last mile" follow-through on infrastructure changes.
Incident Response: Lead capacity-crunch response by rapidly untainting and re-coordinating workloads during high-pressure outages.
Qualifications
Minimum
Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or a related field
5+ years of professional work experience in a high-growth environment, preferably at a hyperscaler (GCP, AWS, Azure) or a specialized GPU provider
Deep expertise in Kubernetes, including hands-on experience with taints, cordons, node draining, and custom operators
Demonstrated experience with Go or Python in a production-level environment
Strong financial literacy and the ability to model complex trade-offs between capacity reliability and cost
High tenacity and collaborative mindset
Preferred
No preferred qualifications listed.