Make intelligence open and accessible to all. Backed by Battery, Lightspeed and Sequoia.
About the role
Build and scale distributed training systems that power frontier model pre-training. Work closely with research teams to design and operate large-scale training runs for foundation models.
What they're looking for
- Experience building or operating distributed training systems for large machine learning models
- Strong experience working with modern distributed training frameworks such as Megatron, DeepSpeed, or similar large-scale training systems
- Familiarity with large-scale model parallelism strategies (data, tensor, pipeline, or expert parallelism)
- Experience optimizing training throughput and GPU utilization in large distributed environments
- Familiarity with GPU communication libraries such as NCCL and performance tuning for distributed workloads
- Experience working closely with ML researchers to productionize experimental training workflows
More about this role
Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.
Build and scale distributed training systems that power frontier model pre-training.
Work closely with research teams to design and operate large-scale training runs for foundation models.
Develop infrastructure that enables efficient training across thousands of GPUs using modern distributed training frameworks.
Optimize training throughput, stability, and efficiency for large model training workloads.
Collaborate directly with pre-training researchers to translate experimental ideas into scalable, production-ready training systems.
Improve performance of distributed training workloads through optimization of communication, memory usage, and GPU utilization.
Build and maintain training pipelines that support large-scale datasets, checkpointing, and experiment iteration.
Debug and resolve performance bottlenecks across distributed training stacks including model parallelism, GPU communication, and training runtime...
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