Thunder Compute, San Francisco, California
Company
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.
Leading solutions for underutilization 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 productionizing 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 LD_PRELOAD, 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.
Role
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:
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.
You will join early enough to meaningfully shape the architecture, engineering standards, and technical direction of the company.
You will work directly with the founders on a category-defining systems problem, with a short path between writing code and seeing it run in production. The systems you build will form the foundation of a new infrastructure layer for GPU computing.
Thunder ComputeSan Francisco
$250K–$325K / yr
Posted 8 days ago
Cerebras SystemsToronto, CAN +1
Pay not posted
Posted 20 days ago
Cerebras SystemsSunnyvale, CA +1
Pay not posted
Posted Nov 25, 2025
ByteDanceSan Jose, California
Pay not posted
$285K–$315K / yr
Posted Aug 29, 2026
$225K–$275K / yr
Posted Aug 29, 2026
Source: the Y Combinator job board. We last checked this job 6 h ago. We first saw it 4 days ago. A job on a job board does not always mean that the team interviews people now.
This base pay is above the median for software engineering jobs in the Bay Area with no level in the job title.
We compare the middle of each posted range. This is base pay as posted. The group does not include this job.