--- title: 'Research Scientist at Pluralis Research' canonical: 'https://feeny.ai/job/research-scientist-pluralis-research-usa-or-v7z1rqs3sas3' type: 'job' last_seen: '2026-09-14' --- # Research Scientist at Pluralis Research - **Company:** Pluralis Research - **Location:** Usa OR, Australia - **Employment:** full-time - **Work type:** remote - **Posted:** 2026-08-31 - **Last confirmed live:** 2026-09-14 - **Apply:** https://jobs.ashbyhq.com/pluralis-research/3f573ea4-5f34-471f-a05e-2561ecb10547 ## Job description 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/). This setting breaks nearly every assumption of datacenter training and inference: communication-efficient training across different parallelism axes, fault tolerance as nodes join and drop mid-run, heterogeneous compute and networks, and robustness to malicious participants. Our published methods include [Subspace Networks](https://arxiv.org/abs/2506.01260), [Factored Gossip DiLoCo](https://arxiv.org/abs/2606.22768), [AsyncMesh](https://arxiv.org/abs/2601.22442), and [Sentinel](https://arxiv.org/abs/2603.03592). As a Research Scientist you work on the problems that stay open as we push from the 8B run toward frontier scale, and you publish what you find. These are foundational papers up for grabs. ## Key Responsibilities - Solve the open problems: Identify the questions that block Protocol Learning at scale. communication efficiency, convergence under churn and staleness, heterogeneity, robustness to malicious participants. - Publish in Tier-1 venues: The problems are hard and largely unclaimed. Solve them, and publish. - Get the methods into runs: Work with the engineering team so your results land in live training runs, not just papers. ## What We're Looking For - Research excellence (required): PhD in machine learning with publications in top-tier conferences (NeurIPS, ICML, ICLR). - Distributed ML experience: Hands-on experience in large-scale distributed training and compression strategies. - Implementation skills: Strong programming ability in PyTorch. - Mission alignment: You believe Protocol Learning is the viable third path for collective, trustless, and sovereign AI. ## Nice to Have - Experience with foundation model pre-training, post-training, or RL. - 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 view AI and the world on a better path for everyone if we are able to implement the protocol for intelligence. ## About Pluralis Research ## Company Overview - **One-liner**: Pluralis Research is developing protocol learning to enable decentralized, collectively-owned and collaboratively-trained frontier AI models that are unextractable and operate over open, permissionless networks. - **Entity Type**: Private (Seed stage) - **Headquarters**: San Francisco, California, United States - **Founded**: 2024 - **Founders**: Alexander Long (Founder and CEO); Founding Scientists include Ajanthan Thalaiyasingam, Gil Avraham, and Sameera Ramasinghe. ## Core Business - **Primary Industry**: Foundational AI Research (Decentralized AI / Protocol Learning) - **Target Customers**: B2B (research institutions, compute providers, open-source community contributors); B2C indirectly (future users of collectively owned models) - **Mission/Purpose**: To create a third path for AI development beyond closed-source models and open-weight models—a self-sustaining, collectively owned ecosystem where models are trained and governed by a community, not a single corporation. ## Products & Services - **Protocol Learning (Core Protocol)**: A novel training paradigm where large AI models are split across geographically separated devices connected only by standard internet links. - **Type**: Research Protocol / Decentralized Infrastructure - **Key Innovation**: Models exist only within the protocol, not on any single device. They are trainable and usable but **unextractable**, preventing any single entity from capturing the model weights. - **Node0**: A collaborative event powered by Protocol Learning, enabling decentralized AI training runs. - **Type**: Open-source tool / Event Framework - **Agora**: A collaborative training library for multi-participant model training. - **Type**: Open-source Library (GitHub) - **AsyncPP & AsyncMesh**: Communication-efficient pipeline and mesh parallelism techniques for scaling decentralized models over low-bandwidth networks (achieving >95% compression and matching centralized wall-clock convergence). - **Type**: Open-source Algorithms (GitHub) ## Market Standing - **Valuation**: Not publicly disclosed - **Total Funding**: $8.6 million - **Non-Equity Assistance** (Nov 2025): $1.0M led by Amazon Web Services - **Pre-Seed Round** (Jan 2025): Led by CoinFund - **Seed Round** (Mar 2025): $7.6M co-led by Union Square Ventures and CoinFund, with participation from Topology, Variant, Eden Block, and Bodhi Ventures. Angel investors include Balaji Srinivasan and Clem Delangue (co-founder of Hugging Face). - **Notable Investors/Partners**: Union Square Ventures, CoinFund, Amazon Web Services, Balaji Srinivasan, Clem Delangue (Hugging Face) - **Growth Signals**: - Headcount grew **+100% YoY** (from ~7 to 17 employees). - Published multiple papers at **NeurIPS 2025** on decentralized training (e.g., Subspace Networks, Mixtures of Subspaces). - 10 active job openings across Research, ML Engineering, and Distributed Systems. - Team has deep FAANG/Anthropic pedigree (Google, Amazon, Oracle, Atlassian alumni). ## Competitive Advantages - **Technical Moat (Unextractable Models)**: Their core IP ensures model weights are never materialized on any participant's device, preventing theft or centralization even during training. - **Economic Moat (Self-Sustaining Economics)**: The protocol allows value to flow programmatically to contributors, solving the financial sustainability problem that plagues purely open-weight models. - **Published Research**: Validated by acceptance at NeurIPS 2025, demonstrating academic credibility and technical rigor. - **Talent Density**: The team comprises PhD researchers who previously worked together at top AI labs (Anthropic, Google, Amazon), with deep expertise in distributed systems and ML. ## Strategic Focus - **Hiring in All Areas**: Currently recruiting across Research, ML Training Platforms, and Distributed ML Systems to accelerate protocol scaling. - **Scaling Decentralized Networks**: Aiming to prove that model-parallel training over low-bandwidth (30Mbps) internet is feasible for billion-parameter models. - **Building Community**: Opening participation in training runs to external contributors, moving toward a truly permissionless network. ## Why Work Here - **Culture of Radical Openness**: The mission is to democratize AI ownership. Work here is published openly, and researchers contribute to a public good. - **High Impact, Small Team**: With only 17 people, every hire has an outsized influence on shaping the protocol and company direction. - **Research First**: The team is PhD-heavy and publishes at top-tier conferences (NeurIPS). The environment is scholarly and technically deep. - **Remote-Flexible (Hybrid)**: Presence in San Francisco (US) and Australia (largest cohort of 12 employees). The job postings do not mandate 5 days in-office; distributed collaboration is core to the product itself. - **Top-Tier Investor Backing**: Backed by USV and CoinFund, providing strong financial runway and network effects in both the AI and crypto/Web3 ecosystems. - **Founding Team Pedigree**: Work alongside former researchers from Anthropic, Google, and Amazon, creating a steep learning curve for ML engineers and scientists. ## Sources 1. [pluralis.ai](https://pluralis.ai/) 2. [LinkedIn - Pluralis Research](https://www.linkedin.com/company/pluralis-research) 3. [GlobeNewswire - Seed Round Announcement](https://www.globenewswire.com/news-release/2025/03/19/3045635/0/en/Pluralis-Research-Pioneers-Protocol-Learning-to-Scale-Decentralized-AI-Announces-7-6M-Seed-Round-Led-by-USV-and-CoinFund.html) 4. [GitHub - Pluralis Research](https://github.com/PluralisResearch) 5. [Jobs.ashbyhq.com - Pluralis Research Careers](https://jobs.ashbyhq.com/pluralis-research) ## Other roles at Pluralis Research - [Executive Assistant](https://feeny.ai/job/executive-assistant-pluralis-research-sydney-w4jd5shemgqs) — Sydney, Australia - [Operations Associate](https://feeny.ai/job/operations-associate-pluralis-research-melbourne-7w6xwzx7zp0q) — Melbourne, Australia - [Machine Learning Engineer - ML Training Platform](https://feeny.ai/job/machine-learning-engineer-ml-training-platform-pluralis-research-usa-or-cb0b1g58vsjh) — Usa OR, Australia - [Research Engineer Intern](https://feeny.ai/job/research-engineer-intern-pluralis-research-australia-62bya07kzppe) — Australia - [Research Scientist Intern](https://feeny.ai/job/research-scientist-intern-pluralis-research-usa-or-gc7q96xt6kh1) — Usa OR, Australia - [Research Engineer - Decentralized Training and Inference Verification](https://feeny.ai/job/research-engineer-decentralized-training-and-inference-verification-pluralis-7gw1639gefck) — Usa OR, Australia - [Research Engineer - Geo-Distributed Inference](https://feeny.ai/job/research-engineer-geo-distributed-inference-pluralis-research-usa-or-gk4y7fqhyk80) — Usa OR, Australia - [Research Engineer - Post-Training](https://feeny.ai/job/research-engineer-post-training-pluralis-research-usa-or-dvzaxxpvrv0x) — Usa OR, Australia - [Research Engineer - Pre-training](https://feeny.ai/job/research-engineer-pre-training-pluralis-research-usa-or-55zxjrp2f7s5) — Usa OR, Australia - [Academic Collaboration](https://feeny.ai/job/academic-collaboration-pluralis-research-sydney-kwpagzyta862) — Sydney, Australia