Member of Technical Staff, Distributed Systems

About the role

Radical Numerics is seeking a Member of Technical Staff to design, build, and operate the data‑center and cloud infrastructure that powers large‑scale training and inference for cutting‑edge biological AI models. You will enable researchers to move fast at scale by delivering reliable, high‑performance compute.

Key responsibilities

• Operate and automate large GPU clusters, handling provisioning, imaging, capacity planning, uptime, utilization, and cost efficiency.
• Build a unified compute interface that abstracts cluster management for training and inference, allowing scientists to focus on research.
• Extend scheduling/orchestration systems (Kubernetes, Slurm) for topology‑aware placement, preemption, quotas, and fair‑share multi‑tenancy.
• Profile and tune performance across the stack, including communication patterns, memory efficiency, custom kernels, and compilation paths.
• Establish robustness standards, monitoring, fault tolerance, checkpointing, and incident analysis for large‑scale research workloads.
• Design durable storage and artifact paths with clear retention and lineage for reproducible experiments.
• Collaborate closely with model researchers and training scientists to unblock large‑scale runs and advise on performance trade‑offs.

Required profile

• Proven experience building or operating large‑scale GPU clusters and container orchestration systems such as Kubernetes or Slurm.
• Strong systems background covering Linux, networking, and infrastructure‑as‑code.
• Deep understanding of modern deep‑learning frameworks and their internals (e.g., PyTorch, Triton, CUDA, C++).
• Ability to debug complex, multi‑layered distributed training systems and performance regressions.
• Excellent written and verbal communication skills bridging technical and scientific domains.

Required skills

• Python
• Rust
• Kubernetes
• Slurm
• Linux
• Networking
• Infrastructure‑as‑code
• PyTorch
• Triton
• CUDA
• C++

What we offer

• Competitive compensation and comprehensive benefits.
• Support for continual learning and professional development.
• Opportunity to work on frontier AI systems with real biological impact.