Moonlite AI

Moonlite AI

Senior Software Engineer, Compute Platform

Chicago, IL or Remote · Senior

Sponsorship not specified$165k-$225kDetected 62 days ago
PythonGoC++Node.jsFastAPIAlgorithmsDockerKubernetesTerraformLinuxPlatform EngineeringgRPCMachine LearningTensorFlowPyTorchResearchCommunicationProblem Solving

About the 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

Responsibilities

  • Design and build scalable compute orchestration platforms that manage GPU clusters, bare-metal server provisioning, and resource allocation across co-located infrastructure environments.
  • Implement intelligent workload scheduling, resource allocation, and optimization algorithms that maximize GPU utilization while maintaining performance guarantees for research and training workloads.
  • 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.
  • 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.
  • Build automation and tooling for complete bare-metal server lifecycle management - from initial provisioning and configuration through ongoing operations, updates, and resource reallocation.
  • Develop robust APIs and SDKs that enable researchers to programmatically provision and manage compute resources, integrating seamlessly with existing workflows and research infrastructure.
  • Build enterprise-grade multi-tenant compute isolation, security boundaries, and resource quotas that enable safe sharing of GPU infrastructure across teams and organizations.
  • Your work will create the platform foundation that enables financial institutions to harness AI capabilities previously impossible with traditional infrastructure.
  • 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.

Requirements

  • 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.
  • GPU Computing Fundamentals: Understanding of GPU architectures, CUDA programming (where/when needed), and GPU resource management - or a strong ability to learn quickly.
  • Problem-Solving & Architecture: Demonstrated ability to solve complex performance and scalability challenges while balancing pragmatic shipping with good long-term architecture.
  • Strong experience with Linux in production environments, including systems for programming, performance optimization, and low-level resource management.
  • Deep knowledge of virtualization technologies (KVM, Xen), container runtimes, and orchestration platforms.
  • Understanding of GPU architectures, CUDA programming (where/when needed), and GPU resource management - or a strong ability to learn quickly.
  • Experience with bare-metal provisioning, out-of-band management systems, and hardware abstraction layers.
  • Demonstrated ability to solve complex performance and scalability challenges while balancing pragmatic shipping with good long-term architecture.

Nice to have

  • Go, C/C++, Python, KVM, Docker, Kubernetes,, NVIDIA GPUDirect, SR-IOV, NVIDIA vGPU, CUDA, InfiniBand, RDMA, Terraform, FastAPI, gRPC, Linux systems programming
  • Hands-On Ownership: As an early engineer, you'll have end-to-end ownership of projects and the autonomy to influence our product and technology direction.

Skills

  • Experience with Go, C/C++, Python, or Rust for performance-critical components is highly valued.
  • Virtualization & Containers: Deep knowledge of virtualization technologies (KVM, Xen), container runtimes, and orchestration platforms.
  • Bare-Metal Infrastructure: Experience with bare-metal provisioning, out-of-band management systems, and hardware abstraction layers.
  • Commitment to Growth: Growth mindset with continuous focus on learning and professional development.
  • Background provisioning or managing research computing environments (Kubernetes, SLURM, or HPC clusters)
  • Experience with GPU virtualization technologies (SR-IOV, NVIDIA vGPU) and multi-tenant GPU sharing
  • Background in container orchestration platforms with custom scheduling or resource management
  • Knowledge of high-performance networking for GPU communication (InfiniBand, RDMA, NVLink, NVSwitch)
  • Familiarity with AI/ML training frameworks (PyTorch, TensorFlow) and their infrastructure requirements
  • Understanding of distributed training patterns and multi-node GPU coordination
  • Background in financial services or other regulated industry infrastructure is a plus
  • Key Technologies

Compensation

  • We offer a competitive total compensation package combining a competitive base salary, startup equity, and industry-leading benefits.

Benefits

  • Implement comprehensive monitoring and telemetry systems for compute resources, providing visibility into GPU virtualization, workload performance and infrastructure health.
  • We offer a competitive total compensation package combining a competitive base salary, startup equity, and industry-leading benefits.
  • The total compensation range for this role is $165,000 - $225,000, which includes both base salary and equity.
  • We provide generous benefits, including a 6% 401(k) match, fully covered health insurance premiums, and other comprehensive offerings to support your well-being and success as we grow together.
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This listing is sourced directly from Moonlite AI's careers page and normalized into a canonical job model.