Senior Software Engineer, Compute Platform

Moonlite delivers high-performance AI infrastructure for organizations running intensive computational research, large-scale model training, and demanding data processing workloads.We provide infrastructure deployed in our facilities or co-located in yours, delivering flexible on-demand or reserved compute that feels like an extension of your existing data center. Our team of AI infrastructure specialists combines bare-metal performance with cloud-native operational simplicity, enabling research teams and enterprises to deploy demanding AI workloads with enterprise-grade reliability and compliance.

Your Role:

You will be instrumental in building out our GPU-accelerated compute platform that powers distributed AI training and inference, large-scale simulations, and computational research workloads. Working closely with product, your platform team members, and infrastructure specialists, you’ll design and implement the compute orchestration layer that manages GPU clusters, bare-metal provisioning, and resource scheduling-enabling researchers and engineers to programmatically access high-performance compute resources with cloud-like simplicity.

Job Responsibilities

Compute Orchestration Systems: Design and build scalable compute orchestration platforms that manage GPU clusters, bare-metal server provisioning, and resource allocation across co-located infrastructure environments.

Resource Management & Scheduling: Implement intelligent workload scheduling, resource allocation, and optimization algorithms that maximize GPU utilization while maintaining performance guarantees for research and training workloads.

Research Cluster Provisioning: Design and implement systems for provisioning and managing research computing environments including Kubernetes and SLURM clusters, enabling automated deployment, resource scheduling, and workload orchestration for distributed AI training and HPC workloads.

GPU Platform Engineering: Develop platform capabilities for managing latest-generation NVIDIA GPU configurations (H100, H200, B200, B300), including GPU resource management, multi-tenant isolation, and integration with compute orchestration systems.

Bare-Metal Lifecycle Management: Build automation and tooling for complete bare-metal server lifecycle management – from initial provisioning and configuration through ongoing operations, updates, and resource reallocation.

Performance-Critical Systems: Optimize compute platform components for high-throughput and low-latency performance, ensuring research workloads achieve near-bare-metal efficiency in virtualized or containersized environments.

Platform APIs & Integration: Develop robust APIs and SDKs that enable researchers to programmatically provision and manage compute resources, integrating seamlessly with existing workflows and research infrastructure.

Observability & Monitoring: Implement comprehensive monitoring and telemetry systems for compute resources, providing visibility into GPU virtualization, workload performance and infrastructure health.

Multi-Tenancy and Isolation: Build enterprise-grade multi-tenant compute isolation, security boundaries, and resource quotas that enable safe sharing of GPU infrastructure across teams and organizations.

Requirements

Experience: 5+ years in software engineering with proven experience building compute platforms, container orchestration systems, or distributed compute infrastructure for production environments.

Compute Platform Engineering: Strong background in building compute orchestration, resource scheduling, or workload management systems at scale.

Kubernetes & Container Orchestration: Strong familiarity with Kubernetes architecture, container orchestration concepts, and experience deploying workloads in Kubernetes environments. Understanding of pods, deployments, services, and basic Kubernetes operations.

Programming Skills: Experience with Go, C/C++, Python, or Rust for performance-critical components is highly valued.

Linux & Systems Programming: Strong experience with Linux in production environments, including systems for programming, performance optimization, and low-level resource management.

Virtualization & Containers: Deep knowledge of virtualization technologies (KVM, Xen), container runtimes, and orchestration platforms.

GPU Computing Fundamentals: Understanding of GPU architectures, CUDA programming (where/when needed), and GPU resource management – or a strong ability to learn quickly.

Bare-Metal Infrastructure: Experience with bare-metal provisioning, out-of-band management systems, and hardware abstraction layers.

Problem-Solving & Architecture: Demonstrated ability to solve complex performance and scalability challenges while balancing pragmatic shipping with good long-term architecture.

Autonomy & Communication: Comfortable navigating ambiguity, defining requirements collaboratively, and communicating technical discussions through clear documentation.