You will own the infrastructure that powers our R&D and helps our customers deploy our technology on-premise. You will move beyond standard cloud DevOps into the world of High-Performance Computing (HPC).
Think: Design a robust CI/CD strategy that handles cross-platform compilation (Windows/Linux) and execution on specific hardware targets (NVIDIA A100, AMD MI250, Consumer GPUs). Architect solution templates for our customers who need to deploy Hybridizer-generated binaries on their own private clouds.
Implement:
Set up and maintain Kubernetes clusters (both on-premise and cloud) with GPU Passthrough and Multi-Instance GPU (MIG) configurations.
Develop GitHub Actions pipelines that seamlessly dispatch heavy test suites to self-hosted runners equipped with specific GPU accelerators.
Configure DockerHub registries and secure container lifecycles for our compiler images.
Build:
Hardware Tuning: Assemble and fine-tune physical servers. This includes managing PCIe topology, cooling profiles, and power constraints to ensure consistent benchmarking results.
Driver Ecosystem: Manage the complex matrix of NVIDIA drivers, CUDA toolkits, and ROCm versions across our fleet, ensuring compatibility with our compiler’s output.
You are a DevOps engineer who loves hardware. You understand that "the cloud" is just someone else's computer, and sometimes you need to manage that computer yourself.
Core DevOps: Strong mastery of Docker and Kubernetes. You know how to write custom Helm charts and manage stateful sets.
GPU Infrastructure: You have hands-on experience with NVIDIA Container Toolkit or ROCm integration in containers. You understand concepts like PCIe passthrough, IOMMU groups, and GPU orchestration.
CI/CD Automation: Expert in GitHub Actions. You can write complex workflows with matrix strategies and self-hosted runners.
System Administration: You are comfortable with Linux kernel tuning, driver installation (dkms), and diagnosing hardware bottlenecks.
Customer Facing: You have the communication skills to assist clients. You can explain how to expose a GPU to a Docker container to a sysadmin who might not be an expert in HPC.
Adaptability: You are ready to work with a mix of consumer and data-center grade hardware (e.g., configuring a server with 4x RTX 5090s or managing a DGX station).
Skills Required
- Strong mastery of Docker and Kubernetes
- Experience writing custom Helm charts and managing StatefulSets
- Hands-on experience with NVIDIA Container Toolkit or ROCm integration in containers
- Understanding of PCIe passthrough, IOMMU groups, and GPU orchestration
- Expertise with GitHub Actions, including matrix strategies and self-hosted runners
- Comfort with Linux kernel tuning, driver installation using DKMS, and hardware troubleshooting
- Communication skills for assisting customers with GPU container deployments
- Ability to work with consumer and data-center GPU hardware
What We Do
Hybridizer is a software platform for performance portability and GPU acceleration. Its compiler transforms C#/.NET and Java bytecode or high-level code into optimized source code for multicore CPUs and GPUs, allowing developers to use existing codebases without learning CUDA or rewriting applications. The technology supports debugging, profiling, cross-platform deployment, and demanding workloads such as quantitative finance, scientific simulation, and data processing.
