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Role Description

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.

• Conduct end-to-end profiling of training workloads to identify bottlenecks across GPU kernels, NCCL communication, and storage I/O.
• Collaborate with systems engineers to improve scheduling efficiency, collective communication performance, and kernel execution.
• Build and maintain high-fidelity tooling to monitor and visualize MFU, throughput, and cluster uptime.
• Design technical processes (e.g., postmortem reviews, incident response) that help the team operate effectively and avoid repeating performance regressions.

Qualifications

• You love optimizing performance and digging into systems to understand how every layer interacts—from the training loop to the hardware.
• Proven experience running distributed training jobs on multi-GPU systems or HPC clusters.
• Strong programming skills in Python and C++ (Rust or CUDA experience is a major plus).
• Solid understanding of PyTorch, JAX, or TensorFlow, and how large-scale training loops are built.
• Familiarity with modern cloud infrastructure, including Kubernetes and Infrastructure as Code.
• A passion for measuring efficiency rigorously and translating raw profiling data into practical engineering improvements.

Requirements

• Experience with Linux kernel tuning, eBPF, and understanding systems design tradeoffs at the hardware level.
• Hands-on experience with GPUs, TPUs, or Trainium, and the networking libraries that power them (NCCL, MPI, UCX).
• Expertise in security best practices for high-scale infrastructure.
• Familiarity with monitoring tools like Prometheus and Grafana.

Benefits

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.

Company Description

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.
• Today, Andromeda works with leading AI labs, data centers, and cloud providers to deliver compute when and where it’s needed most.
• 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.