Lead C++ Systems Engineer, GPU Virtualization

The world is building massive amounts of GPU capacity. Meanwhile, deployed GPUs are only 20% utilized.

This is because GPUs are not virtualized, while every other type of hardware is. For example CPUs and storage are allocated through virtual abstractions which efficiently manage the physical hardware, while GPUs are statically allocated on a one-to-one basis.

Thunder Compute is building this virtualization layer for GPUs. We have raised over $17.5M from Matrix Partners, Y Combinator, and leading angels from Coreweave, Microsoft, Cognition, and Anthropic.

Leading solutions for under utilization sit at the workload layer and are therefore only able to optimize specific use cases. We believe the ideal cluster optimization solution must be invisible to developers and compatible with all workloads; hence, it must sit at the systems layer.

We are a team of systems researchers product ionizing cutting-edge GPU virtualization research to build this general-purpose optimization layer.

Concretely, our virtualization library abstracts GPUs across TCP networking. We use a userspace shim library, loaded through , to intercept CUDA calls and send them over gRPC to a host server connected to a physical GPU elsewhere in the data center.

This enables something like “Ceph for GPUs”: GPUs become network resources that can be abstracted, pooled, and dynamically allocated across a cluster to improve utilization without requiring developers to modify their workloads.

Your work will focus on building the core C++ systems behind our virtualization layer. This includes low-latency performance optimization, distributed systems debugging, production reliability, and research into new techniques for improving GPU utilization.

You will take ownership of complex systems from early experimentation through production deployment. Example projects may include:

Profiling and reducing latency across remote CUDA operations

Building high-performance networking and data-transfer paths

Debugging failures across customer processes, our userspace runtime, the network, and remote GPU servers

Improving support for process forking, signals, multithreading, dynamic linking, and unusual application behavior

Designing systems for GPU allocation, scheduling, failure recovery, and observability

Researching and product ionizing new GPU virtualization and over subscription techniques

Expanding compatibility across CUDA applications, frameworks, and GPU architectures

You will spend your days bouncing between the weeds of complex, performance-critical systems that are live in production. One week, you may be tracing a synchronization bug across a distributed CUDA workload; the next, you may be redesigning a hot data path to remove microseconds of overhead.

This work is not easy. It blends the hardest parts of systems research and production engineering.

We look for exceptional low-level engineering talent, strong work ethic, and extreme attention to detail. We must move quickly while shipping high-quality, reliable systems code.

Exceptional modern C++ ability, including memory management, concurrency, performance optimization, and systems-level abstraction design

Deep understanding of operating systems, low-level networking, compilers, distributed systems, or computer architecture

Experience building and operating performance-critical C++ systems in production

Strong Linux systems programming and debugging ability

Ability to reason through unfamiliar systems across multiple layers of the stack

Strong work ethic and the ability to independently push a project from an experimental prototype through 100% completion under tight deadlines

Attention to detail and the ability to deliver production-ready, thoroughly tested code without significant oversight

Strong ownership over correctness, reliability, performance, and operational outcomes

Ability to debug ambiguous problems without a clear reproduction, existing playbook, or obvious owner

Willingness to work directly with customers and investigate difficult production failures

Experience with CUDA, GPU systems, compilers, runtime interception, dynamic linking, high-performance networking, or distributed computing

Experience at a trading firm such as Citadel Securities or Jane Street; a hardware or AI infrastructure company such as NVIDIA or Samba Nova; a systems research group; or a similarly demanding engineering environment

Strong computer science fundamentals demonstrated through academic work, systems…