VP of Engineering

**Hyperbolic** VP of Engineering; $300K–$350K + ~1% equity + ~10% bonus; San Francisco (Hybrid)

**About Company**

Hyperbolic Labs is pioneering AI infrastructure with its open-access GPU cloud, aggregating computing resources across the globe to offer an innovative GPU marketplace and AI inference service at up to 75% cost savings compared to traditional cloud providers. The mission is to democratize AI by breaking down barriers to computing power — making it affordable and accessible to developers and researchers everywhere.

Founded by co-founders with PhDs in AI, Math, and Computer Science, Hyperbolic raised a Series A and is preparing for significant growth. The team is ~30 people. The company sits at the intersection of AI and open-source technology, and the culture is engineering-driven with a strong emphasis on ownership, execution, and building at the cutting edge of GPU infrastructure.

**About the Role**

This is Hyperbolic's first dedicated engineering executive hire — building the Infrastructure, Platform, and SRE functions from the ground up. The VP of Engineering will own infrastructure strategy, organizational growth, and executive-level decision making, but this is NOT a step-back-and-manage role. Hyperbolic expects this person to be MORE than 40% hands-on: personally contributing to architecture reviews, debugging critical production issues, and partnering with engineers on implementation. The previous batch of roles saw 29 of 33 submissions rejected for wrong company background — this hire needs to come from the GPU cloud / AI infrastructure world, not traditional enterprise or big tech.

**Responsibilities**

- Lead the design and evolution of the AI cloud platform architecture — GPU orchestration, compute scheduling, networking, storage, and distributed systems - Build and scale large GPU clusters supporting customer workloads, including GPU provisioning, scheduling, utilization optimization, and capacity management - Personally participate in architecture reviews, system design, and key technical initiatives (expect 40%+ of time on technical contribution) - Act as the technical escalation point for complex infrastructure challenges — debug production issues, review proposals, and drive decisions - Establish best practices for Kubernetes, observability, CI/CD, security, and operational excellence - Build SRE and Platform Engineering functions from scratch — define SLOs, SLIs, incident response, and capacity planning - Recruit and develop world-class Infrastructure, Platform, and SRE teams - Partner with executive leadership on company strategy and infrastructure investments - Manage infrastructure budgets, vendor relationships, and capacity planning

**Required Skills**

A. 12+ years building and operating large-scale infrastructure systems, with experience leading infrastructure organizations while remaining deeply hands-on technically B. Previous experience building or operating a cloud platform at scale — ideally GPU-native cloud infrastructure supporting AI training and inference workloads C. Expert-level Kubernetes knowledge and experience designing multi-region cloud infrastructure D. Deep expertise in Linux, networking, distributed systems, and storage architecture E. Proven track record scaling infrastructure in high-growth startup environments — not just maintaining systems at large companies F. Strong understanding of Infrastructure-as-Code, automation frameworks, observability, monitoring, and reliability engineering G. Experience building highly available production systems with clear SLOs and incident response processes

**Bonus Skills**

- Experience with GPU scheduling, Slurm, Kubernetes GPU operators, Ray, or distributed training systems - Experience managing thousands of GPUs in production environments - Background supporting AI training and inference platforms at scale - Experience with bare-metal provisioning and lifecycle management (IPMI/Redfish, BMC, PXE boot)

**Logistical Info**

- Location: San Francisco, Hybrid — ideally 2–3 days per week in office. US-based required. - Compensation: $300K–$350K base + equity within 1% + ~10% annual bonus. - Number of openings: 1 - **Other:** ~30-person team, Series A stage. Visa: US citizen / green card preferred; open to H-1B transfers.

**Ideal Background**

- Strongest signal — GPU cloud / AI infrastructure companies: CoreWeave, Lambda, SF Compute, Modal, Together AI, RunPod, Crusoe, Shadeform, or similar - Has scaled from early-stage to operating thousands of GPUs in production, while staying deeply technical - Career arc: strong IC engineer → infra/platform lead → head of infrastructure → VP, with the key differentiator being they never fully stopped building - FAANG/big tech backgrounds are a negative signal unless paired with meaningful startup infrastructure experience - Comp note: do not advertise comp externally — frame as "competitive executive comp with meaningful equity" and share specifics only in late-stage conversations. **Green Flags**

- Background at GPU cloud / AI infrastructure companies (CoreWeave, Lambda, SF Compute, Modal, Together AI, Shadeform, RunPod, Crusoe) - Has built infrastructure teams from scratch, not just managed existing ones - Still writes code, reviews architecture, debugs production — not a full-time manager - Deep Kubernetes expertise demonstrated through real operational experience, not just certifications - Has scaled GPU clusters supporting AI training/inference workloads - High agency, low ego — comfortable being both the VP and the senior engineer in the room

**Red Flags**

- Wrong company background — enterprise IT, consulting, non-infrastructure roles at big tech (this is the #1 rejection reason historically) - Management-only track — if they haven't been hands-on technical in the last 2–3 years, they won't fit. This role is MORE than 40% hands-on - Overconfident / high ego — Hyperbolic is a small team where everyone rolls up their sleeves - Pure cloud-native without bare-metal experience — Hyperbolic operates GPU hardware directly, not just AWS/GCP abstractions - No AI/ML infrastructure context — understanding GPU workloads, training pipelines, and inference optimization is critical

**Interview Process**

VP track involves more rounds than the standard 3-round process for other roles. 1. Recruiter screen (Austin Dupuy) 2. Hiring manager / co-founder call 3. Technical deep-dive (architecture, systems design) 4. Additional founder/leadership interviews 5. Onsite / final loop