Research Scientist - Post-Training

Agora gave us a pretrained 8B model. Post-training is how we make it useful for agentic use-cases. But every post-training stack you've seen assumes a datacenter — synchronous rollouts, fast interconnects, trusted workers. Ours gets none of that. It has to run on consumer GPUs, and Macs spread across the public internet, training a model whose weights no single participant ever holds, with rollouts arriving from a geo-distributed inference pipeline at high latencies. Your primary role is to make RL post-training work here anyway — the algorithms and the system, end-to-end.

Key Responsibilities

* Build the post-training stack: You build the RL training loop end-to-end: rollout ingestion from the geo-distributed inference pipeline, reward computation, policy updates, and getting updated weights back out to the network. You set the direction, and you make things happen.

* Invent the algorithms: Standard RL recipes assume on-policy rollouts from fast, trusted hardware. You adapt them to asynchronous, high-latency, partially trusted generation: staleness tolerance, off-policy corrections, and communication-efficient policy updates.

* Ship first post-trained models: You build the evals that show the models are improving, and you take the first decentralized post-trained release from run to public artifact.

What We're Looking For

* 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 So is unpublished work you can defend in detail.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.

Nice to Have

* 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

Compensation & Benefits

* Equity-Heavy Package: We offer significant ownership for key technical contributors in addition to a high base salary.

* Remote-First Culture: Flexible work environment with team members distributed globally.

* Visa Sponsorship: Optional full visa sponsorship and relocation support to either Australia or the US.

* Open Problems: Training and serving frontier models on hardware you don't control, over networks you don't own, mostly has no published answers yet. You'll write some of the first ones.

FYI's

* 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.