Head of GPU Cloud

Blue Signal Search; United States; On-site; USD 200,000 - 300,000; Full time

Job summary

Blue Signal Search is seeking a Head of GPU Cloud to lead the engineering organization responsible for transforming GPU capacity into high-performance cloud services for AI workloads. This role shapes platform architecture, production inference, developer experiences, and engineering strategy at a pivotal growth stage.

You will own Kubernetes-based infrastructure, model serving, and optimization across vLLM, TensorRT LLM, and related stacks, balancing throughput, latency, and cost across

Qualifications

• Extensive leadership of engineering teams delivering mission-critical cloud infrastructure.
• Proven track record optimizing large-scale AI/model serving workloads.
• Deep experience with GPU accelerators, memory management, and high-throughput scheduling.

Responsibilities

• Lead the engineering org behind a nationwide GPU cloud platform.
• Design orchestration policies matching workloads to accelerators by memory, performance, and cost.
• Own Kubernetes-based infrastructure strategy including multi-tenant concerns and reliability.
• Guide product and platform decisions affecting inference services and developer UX.

Skills

GPU cloud leadership

Cloud platforms

Distributed infrastructure

Kubernetes expertise

Model serving optimization

High-performance computing

AI infrastructure

Resource allocation

Memory-aware scheduling

Platform reliability

Tools

Kubernetes

TensorRT LLM

vLLM

TGI

Infra tooling

Job description

Our client is building the software foundation for a next generation accelerated computing platform designed to make large-scale AI infrastructure easier to consume, operate, and scale. They are seeking a Head of GPU Cloud to lead the engineering organization responsible for transforming significant GPU capacity into high-performance cloud services for AI workloads. This is an opportunity to shape platform architecture, production inference, developer experiences, and engineering strategy at a stage where technical decisions will directly influence customer adoption, infrastructure economics, and long-term growth.

This Role Offers

• Opportunity to define the architecture and operating model behind large scale AI inference services.
• Direct influence over GPU utilization, platform economics, customer experience, and technical strategy.
• Close collaboration with leaders across infrastructure, networking, product, operations, and commercial functions.
• A highly technical environment where software engineering intersects with accelerated computing, distributed systems, and AI infrastructure.

Focus

• Build and scale the engineering organization behind a nationwide GPU cloud platform, with ownership across inference services, orchestration, APIs, platform reliability, and technical execution.
• Set the architecture for moving AI workloads efficiently from customer request to accelerator, including routing, placement, model lifecycle management, caching, and memory aware scheduling.
• Lead production model serving and optimization across technologies such as vLLM, TensorRT LLM, and TGI, improving throughput, latency, availability, accelerator utilization, and cost.
• Own Kubernetes based GPU infrastructure strategy across workload scheduling, elasticity, observability, deployment automation, multi-tenant isolation, and operational resilience.
• Develop platform capabilities for hosted models, private inference environments, customized deployments, usage measurement, customer controls, and developer facing services.
• Design orchestration policies that match workloads to accelerators based on memory requirements, performance objectives, capacity, cluster topology, and infrastructure economics.
• Partner with networking, systems, data center, product, and commercial leaders to align software decisions with accelerator architecture, fabric performance, storage, and customer requirements.
• Establish engineering standards, service objectives, capacity planning, incident readiness, and team accountability while recruiting and developing senior technical talent.

Skill Set

• 12 or more years of progressive software engineering experience, including substantial leadership responsibility across cloud platforms, distributed infrastructure, HPC, or similarly complex production systems.
• 5 or more years leading engineering teams responsible for business critical infrastructure, platform services, or other mission critical technology products.
• Demonstrated production experience operating large scale model inference using vLLM, TensorRT LLM, TGI, or equivalent serving stacks.
• Strong expertise in model serving optimization, including dynamic batching, decoding acceleration, reduced precision execution, compilation, memory reuse, and request scheduling.
• Advanced knowledge of Kubernetes and containerized infrastructure, including scheduling, elasticity, telemetry, deployment practices, and production reliability.
• Practical accelerator infrastructure knowledge covering GPU memory behavior, high bandwidth networking, InfiniBand, RoCE, storage performance, and cluster topology.
• Experience architecting highly available distributed platforms with automated resource allocation, programmatic interfaces, multi customer support, and detailed consumption measurement.
• Strong technical and leadership judgment with the ability to balance performance, reliability, security, customer experience, and infrastructure economics across multidisciplinary teams.

Additional Experience That Stands Out

• Leadership experience in GPU cloud, AI infrastructure, hosted model platforms, or accelerated computing environments.
• Experience delivering elastic inference services, dedicated AI capacity, model customization workflows, or managed AI products.
• Familiarity with open model ecosystems and the operational differences among model families, serving configurations, and hardware profiles.
• Track record improving accelerator utilization, workload density, capacity forecasting, and compute economics across multiple GPU generations.
• Experience scaling engineering organizations in fast moving environments where software requirements and infrastructure capacity evolve together.