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Moonlite - Senior Software Engineer, Compute Platform

Remote - Chicago, IL or Remote$165k - $225k2d ago

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. • Compute Platform Engineering: • 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. • Kubernetes & Container Orchestration: • 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. • Linux & Systems Programming: • Virtualization & Containers: Deep knowledge of virtualization technologies (KVM, Xen), container runtimes, and orchestration platforms. • Virtualization & Containers: • GPU Computing Fundamentals: Understanding of GPU architectures, CUDA programming (where/when needed), and GPU resource management – or a strong ability to learn quickly. • GPU Computing Fundamentals: • Bare-Metal Infrastructure: Experience with bare-metal provisioning, out-of-band management systems, and hardware abstraction layers. • Bare-Metal Infrastructure: • Problem-Solving & Architecture: Demonstrated ability to solve complex performance and scalability challenges while balancing pragmatic shipping with good long-term architecture. • Problem-Solving & Architecture: • Autonomy & Communication: Comfortable navigating ambiguity, defining requirements collaboratively, and communicating technical discussions through clear documentation. • Autonomy & Communication: • Commitment to Growth: Growth mindset with continuous focus on learning and professional development. • Commitment to Growth: • 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 • Experience building infrastructure for research institutions,labs, or technical computing environments • Background in financial services or other regulated industry infrastructure is a plus • Key Technologies • Go, C/C++, Python, KVM, Docker, Kubernetes,, NVIDIA GPUDirect, SR-IOV, NVIDIA vGPU, CUDA, InfiniBand, RDMA, Terraform, FastAPI, gRPC, Linux systems programming

Responsibilities

• 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. • 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. • Compute Orchestration Systems: • 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. • Resource Management & Scheduling: • 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. • Research Cluster Provisioning: • 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. • GPU Platform Engineering: • 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. • Bare-Metal Lifecycle Management: • 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. • Performance-Critical Systems: • 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. • Platform APIs & Integration: • Observability & Monitoring: Implement comprehensive monitoring and telemetry systems for compute resources, providing visibility into GPU virtualization, workload performance and infrastructure health. • Observability & Monitoring: • 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. • Multi-Tenancy and Isolation:

Benefits

• Build Next-Generation Infrastructure: Your work will create the platform foundation that enables financial institutions to harness AI capabilities previously impossible with traditional infrastructure. • Build Next-Generation Infrastructure: • 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. • Hands-On Ownership: • Shape Industry Standards: Contribute to defining how enterprise AI infrastructure should work for the most demanding regulated environments. • Shape Industry Standards: • Collaborate with Experts: Work alongside seasoned engineers and industry professionals passionate about high-performance computing, innovation, and problem-solving. • Collaborate with Experts: • Start-Up Agility with Industry Impact: Enjoy the dynamic, fast-paced environment of a startup while making an immediate impact in an evolving and critical technology space. • Start-Up Agility with Industry Impact: