• Work in a small, dedicated team architecting US Navy enterprise systems. • Collaborate with other teams to integrate applications with Defense Unicorns’ software delivery platform. • Help build pipelines that automate testing, building, packaging, and deploying platform components. • Leverage Infrastructure-as-Code to create standardized, reusable modules that automate provisioning across diverse hypervisors and cloud platforms. • Provide grounded time and effort estimates for tasks. Assist in project planning and resource allocation based on estimated timelines. • Validate Solutions/Implementations: Ensure that solutions and implementations align with the outlined tasks and business requirements. • Rapidly understand customer mission requirements, conceptualize and prototype solutions, and rapidly iterate to deliver impact. • Author and maintain technical design documents, architecture decision records (ADRs), and test plans.

• Define AI infrastructure architecture strategy • Lead cross-functional collaboration with Data Science and Security teams • Design multi-region GPU cluster strategy • Evaluate emerging AI infrastructure technologies • Establish best practices and governance models • Design and implement inference efficiency initiatives such as prompt/context caching. • Build systems that allow fine-grained control over cache prefixes and retrieval strategies. • Optimize latency and cost efficiency of large-scale LLM inference workloads. • Support Retrieval-Augmented Generation (RAG) architectures. • Architect and implement end-to-end encryption for cached AI content. • Integrate customer-managed encryption keys (CMEK) within cloud environments. • Ensure secure multi-tenant data isolation and compliance standards. • Develop enterprise-ready vector similarity search systems. • Optimize Approximate Nearest Neighbor (ANN) algorithms for scale and latency. • Build ranking models for personalization, recommendation, and monetization. • Contribute to highly scalable embedding search infrastructure. • Design and maintain petabyte-scale distributed storage systems. • Implement materialized views with consistent cross-datacenter updates. • Support high-update throughput systems with low-latency point queries. • Optimize large-scale table scans and distributed data processing.

Site Reliability Engineer - AI Infrastructure Location: Global Remote / San Francisco · Full-Time About Andromeda Andromeda Cluster was founded by Nat Friedman and Daniel Gross to give early-stage startups access to the kind of scaled AI infrastructure once reserved only for hyperscalers. We began with a single managed cluster — but it filled almost instantly. Since then, we’ve been quietly building the systems, network, and orchestration layer that makes the world’s AI infrastructure more accessible. Today, Andromeda works with leading AI labs, data centers, and cloud providers to deliver compute when and where it’s needed most. Our platform routes training and inference jobs across global supply, unlocking flexibility and efficiency in one of the fastest-growing markets on earth. Our long-term vision is to build the liquidity layer for global AI compute — a marketplace that moves the infrastructure and workloads powering AGI not dissimilar to the flows of capital in the world's financial markets. We are expanding to new frontiers to find the brightest that work in AI infrastructure, research and engineering. What You’ll Do - Provision, configure, and operate Kubernetes-based clusters for customers across multiple providers. - Build automation and tooling to streamline cluster deployments and integrations. - Debug customer issues across networking, storage, scheduling, and system layers. - Improve reliability and scalability of both training and inference infrastructure. - Design and implement monitoring, alerting, and observability for critical systems. - Collaborate with engineering and product teams to plan and deliver infrastructure for new services. - Participate in on-call and incident response, leading postmortems and reliability improvements. What We’re Looking For - 5+ years experience in SRE, DevOps, or infrastructure engineering roles. - Strong Linux systems and networking fundamentals. - Deep experience with Kubernetes and container orchestration at scale. - Proficiency with Infrastructure-as-Code (Terraform, Helm, Ansible, etc.). - Strong automation and scripting skills (Python, Go, or Bash). - Experience with observability stacks (Prometheus, Grafana, Loki, Datadog, etc.). - Track record of operating production systems and leading incident response. Nice to Have - Exposure to ML/AI infrastructure or GPU-based systems (CUDA, Slurm, Triton, etc.). - Familiarity with high-performance networking (InfiniBand, NVLink) or distributed storage (VAST, Weka, Ceph). - Customer-facing support or consulting experience. Why You’ll Love It Here This is a builder’s role. You’ll have ownership and autonomy to shape how our systems run, working directly with customers and providers while building the foundation for reliable, scalable AI infrastructure.

Performance Engineer - AI Infrastructure Location: Global Remote / San Francisco · Full-Time About Andromeda Andromeda Cluster was founded by Nat Friedman and Daniel Gross to give early-stage startups access to the kind of scaled AI infrastructure once reserved only for hyperscalers. We began with a single managed cluster — but it filled almost instantly. Since then, we’ve been quietly building the systems, network, and orchestration layer that makes the world’s AI infrastructure more accessible. Today, Andromeda works with leading AI labs, data centers, and cloud providers to deliver compute when and where it’s needed most. Our platform routes training and inference jobs across global supply, unlocking flexibility and efficiency in one of the fastest-growing markets on earth. Our long-term vision is to build the liquidity layer for global AI compute. We are expanding to new frontiers to find the brightest that work in AI infrastructure, research and engineering. The Opportunity We are hiring a Performance Engineer to join our Growth team. In this role, your "product" is the efficiency and throughput of our massive-scale AI clusters. As we scale our network, the difference between a "working" cluster and an "optimized" one represents millions of dollars in value and weeks of saved research time for our customers. You will sit at the intersection of systems engineering and research, profiling end-to-end training runs to hunt down bottlenecks in compute, communication, and storage. What You’ll Do - Profile & Optimize: Conduct end-to-end profiling of training workloads to identify bottlenecks across GPU kernels, NCCL communication, and storage I/O. - System Refinement: Collaborate with systems engineers to improve scheduling efficiency, collective communication performance, and kernel execution. - Observability: Build and maintain high-fidelity tooling to monitor and visualize MFU, throughput, and cluster uptime. - Process Design: Design technical processes (e.g., postmortem reviews, incident response) that help the team operate effectively and avoid repeating performance regressions. What We’re Looking For - Systems Intuition: You love optimizing performance and digging into systems to understand how every layer interacts—from the training loop to the hardware. - Distributed Training Experience: Proven experience running distributed training jobs on multi-GPU systems or HPC clusters. - Coding Proficiency: Strong programming skills in Python and C++ (Rust or CUDA experience is a major plus). - ML Framework Depth: Solid understanding of PyTorch, JAX, or TensorFlow, and how large-scale training loops are built. - Infrastructure Knowledge: Familiarity with modern cloud infrastructure, including Kubernetes and Infrastructure as Code. - Rigor: A passion for measuring efficiency rigorously and translating raw profiling data into practical engineering improvements. Strong Candidates May Have - Low-Level Mastery: Experience with Linux kernel tuning, eBPF, and understanding systems design tradeoffs at the hardware level. - Specialized AI Infra: Hands-on experience with GPUs, TPUs, or Trainium, and the networking libraries that power them (NCCL, MPI, UCX). - Security & Privacy: Expertise in security best practices for high-scale infrastructure. - Observability: Familiarity with monitoring tools like Prometheus and Grafana. Why You’ll Love It Here This is a builder’s role. You’ll have ownership and autonomy to shape how our systems run, working directly with customers and providers while building the foundation for reliable, scalable AI infrastructure. Andromeda Cluster is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.