Research Engineer - Post-Training

Hands-on RL post-training : You've run RL post-training on large language models — RLHF, RLVR, or reasoning-focused RL — and touched the systems layer yourself: rollout generation, async training loops, weight synchronization. Not just launched jobs on someone else's stack.

Strong engineering : Production-quality Python and PyTorch: concurrency, failure handling, profiling before optimizing.

Research ability : Publications in RL post-training, asynchronous or distributed RL, or nearby fields are a strong signal. So is unpublished work you can defend in detail.

Mission alignment : You believe Protocol Learning is the viable third path for collective, trustless, and sovereign AI.

Experience training over slow networks, or with decentralized or federated setups.

Familiarity with serving-engine internals such as vLLM or SGLang — our rollout pipeline is a serving system.

Experience with reward modeling or building verifiable-reward datasets.

Experience with P2P networking and NAT traversal.

Experience at proprietary, open-weight and open-source AI labs

We work remotely across the world, with the main teams in Australia and North America. You'll need to be comfortable working across timezones.

Applicants must have professional-level English proficiency (written and spoken).

Recruiters: we aren't looking for agency support at this time. We'll reach out if we need help.

We are backed by Union Square Ventures and other tier-1 investors, and we are a world-class, deeply technical team of ML researchers. Pluralis is unapologetically ideological. We believe AI, and the world, end up on a better path if we succeed in implementing the protocol for intelligence. If this resonates, please apply.