Research Engineer - Post-Training [Remote]

Pluralis Research works on Protocol Learning: training and serving large models in a fully decentralized way on small consumer-grade devices connected via the internet. Despite being dismissed as infeasible, we have made significant advances on this problem, most recently Agora, a permissionless run that pretrained an 8B model from scratch on consumer GPUs spread over the internet, with no single participant ever holding the full weights ([tech report]( While many of the core research problems have been solved, Protocol Learning unlocks a series of new challenges. For the mission in full, read [A Third Path: Protocol Learning](

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.