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Key Responsibilities:
- Distributed pretraining: Implement and optimize model-parallel training with data, pipeline, and tensor parallelism.
- Performance optimization: Apply techniques to reduce communication overhead while preserving convergence.
- Elasticity and fault tolerance: Build robust checkpointing and state synchronization for dynamic node participation.
What We're Looking For:
- Hands-on distributed training across many devices with PyTorch, FSDP, DeepSpeed, or Megatron.
- Strong engineering: production-quality Python, concurrency, failure handling, and profiling.
- Evidence of execution: shipped systems, research code, open-source work, or serious personal projects.
Nice To Have:
- Experience training or serving large language models like Nemotron, Qwen, or OLMo.
- Familiarity with P2P networking and NAT traversal.
- Experience with post-training, RL, inference, or serving systems.
Compensation & Benefits:
- Equity-heavy package with significant ownership for key contributors.
- Remote-first culture with flexible work and globally distributed team.
- Optional visa sponsorship and relocation support to Australia or the US.
Pluralis Research
Pluralis Research is a decentralized AI research company developing Protocol Learning to train large models on consumer devices across the internet. The team is world-class and deeply technical, backed by tier-1 investors, with a remote-first culture.