Member of Technical Staff, Distributed Systems

The role

You will design, build, and operate the data center and cloud environment for large-scale training and inference. This role involves managing GPU clusters and building software to abstract cluster management for researchers.

What you will do

• Operate and automate large GPU clusters.
• Own provisioning, imaging, and capacity planning.
• Build a unified compute interface for training and inference.
• Extend scheduling and orchestration using systems like Kubernetes or Slurm.
• Maximize throughput and hardware efficiency through profiling and tuning.
• Improve reliability and recovery through monitoring and fault tolerance.
• Build reliable storage and artifact paths for datasets and checkpoints.
• Collaborate with model researchers and training scientists.

Required qualifications

• Track record building distributed systems, or operating large-scale GPU clusters and container orchestration systems such as Kubernetes or Slurm.
• Proficiency in building performant, maintainable software in at least one backend language (Python or Rust).
• Strong systems background spanning Linux, networking, and infrastructure-as-code.
• Strong understanding of modern deep learning frameworks and their systems internals (e.g., PyTorch, Triton, CUDA, C++).
• Ability to debug complex, multi-layered systems involving distributed training, memory and performance regressions, and reliability issues in large codebases.
• Comfort operating across the stack and owning projects end to end.
• Excellent written and verbal communication skills bridging technical and scientific domains.

Nice to have

• Familiarity with CUDA/NCCL and performance profiling for distributed training and inference.
• Experience supporting large-scale distributed training for frontier or foundation models.
• Contributions to open-source ML systems or infrastructure such as PyTorch, Torchtitan, or Megatron-LM.
• Familiarity with ML runtimes, compilers, numerics, communication libraries, and custom kernel development.
• Experience improving researcher productivity through infrastructure design, developer tooling, or workflow improvements.
• Background in applied math, systems, computational biology, or related quantitative sciences.

Benefits

• Competitive compensation
• Comprehensive benefits
• Support for continual learning

Before you apply

• Radical Numerics participates in E-Verify to confirm authorization to work in the U.S.

About the company

Radical Numerics is an AI research lab building general biological intelligence. Their mission is to master the code of life to reduce human suffering.