Member of Technical Staff — Training
About The Position
RadixArk is seeking a Member of Technical Staff — Training to build and scale the systems that train frontier AI models. You will work on large-scale distributed training infrastructure for LLMs and generative models, pushing the limits of scale, efficiency, accuracy and reliability across 10k, or 100k+ of GPUs. This role sits at the intersection of ML, systems, and performance engineering. Your work will directly impact how next-generation AI models are trained and scaled. This is a deeply technical, high-impact role for engineers who enjoy solving hard systems problems at extreme scale.
Requirements
- 3+ years of experience in ML systems, or large-scale training infrastructure
- Experience building or operating large-scale agentic post-training systems.
- Experience working on training / inference correctness or other precision-related problem
- Experience debugging performance and stability issues in large post-training jobs
- Experience improving training or inference efficiency.
Nice To Haves
- Experience training 100+ billion-parameter models
- Experience with train / inference optimization for large-scale RL or other production workload.
- Familiarity with training stacks (e.g. Megatron-LM, FSDP, torchtitan, etc.) and inference stack (e.g. SGLang, vLLM, etc.)
- Familiarity with post-training framework (e.g. Miles, Slime, veRL, Prime-RL, AReaL, etc.)
- Experience with RDMA, InfiniBand, NVLink, NCCL/RCCL, or high-speed GPU interconnects
- Contributions to ML systems open-source projects
- Experience with checkpointing, fault recovery, and elastic training.
- Experience building infrastructure for agentic post-training, such as async rollout pipelines, sandbox, or harness system.
Responsibilities
- Contribute to open-source large-scale post-training infrastructure Miles, and inference system SGLang.
- Optimize throughput, scalability, and hardware efficiency
- Improve reliability and fault tolerance for long-running training jobs
- Develop training frameworks and infrastructure tooling
- Collaborate with model researchers to support frontier experiments
- Debug and resolve cross-layer performance bottlenecks
- Build observability systems for training performance and reliability
- Drive capacity planning and cluster utilization strategies
- Contribute to long-term training infrastructure architecture
Benefits
- competitive compensation with meaningful equity
- comprehensive benefits
- flexible work arrangements
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
