---
title: "Research Engineer - Post-Training"
company: "Pluralis Research"
company_url: "https://www.remjobs.works/companies/pluralis-research"
url: "https://www.remjobs.works/job/pluralis-research-research-engineer-post-training-cc292a9f-ca36-4819-ae4e-c4de51f942f4"
apply_url: "https://jobs.ashbyhq.com/pluralis-research/37a272e7-f55c-42e6-8db8-5c94f9934d86"
workplace: remote
location: "San Francisco"
remote_scope: "San Francisco"
employment_type: full-time
seniority: mid
role: ai-machine-learning
region: united-states
skills: ["llm", "python", "pytorch"]
date_posted: 2026-08-31T13:59:43.382Z
first_seen_by_remjobs: 2026-09-17T11:39:12.860Z
---

# Research Engineer - Post-Training

**Pluralis Research** · San Francisco

Apply: https://jobs.ashbyhq.com/pluralis-research/37a272e7-f55c-42e6-8db8-5c94f9934d86

## About the role

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](https://arxiv.org/abs/2607.13332)). 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](https://pluralis.ai/blog/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.*We are backed by *[Union Square Ventures](https://www.usv.com/)* 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.*

---

Source: Pluralis Research's own career page, read by RemJobs. Canonical HTML version: https://www.remjobs.works/job/pluralis-research-research-engineer-post-training-cc292a9f-ca36-4819-ae4e-c4de51f942f4