--- title: 'Research Engineer - Decentralized Training and Inference Verification at Pluralis Research' canonical: 'https://feeny.ai/job/research-engineer-decentralized-training-and-inference-verification-pluralis-7gw1639gefck' type: 'job' last_seen: '2026-09-07' --- # Research Engineer - Decentralized Training and Inference Verification at Pluralis Research - **Company:** Pluralis Research - **Location:** Usa OR, Australia - **Employment:** full-time - **Work type:** remote - **Posted:** 2026-08-31 - **Last confirmed live:** 2026-09-07 - **Apply:** https://jobs.ashbyhq.com/pluralis-research/fb000d3c-9464-4230-9876-0910f20e5109 ## 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/. Our training and inference network is trustless, and the workers are GPUs scattered across the world. Many things can go wrong in this system. An inference worker can return tokens from a cheaper model, or a highly quantized version of the one it's supposed to run. Even with the right model, it can sample with the wrong parameters. Training faces its own attacks, and Agora showed what a permissionless run deals with in practice: participants who disrupt training by dropping updates or flooding the system, free-riders who submit trivial work and collect the rewards, poisoned updates that plant backdoors, attempts to extract private data from gradients and activations, and contributors who inflate their reported work to claim rewards they didn't earn. Sentinel https://arxiv.org/abs/2603.03592 is our first published answer on the training side. Your primary role is to come up with efficient algorithms and systems that verify the work: that tokens came from the claimed model and sampling parameters, and that training contributions are what they claim to be. ## KEY RESPONSIBILITIES - Own the threat model: You enumerate what a malicious or careless worker can do across pre-training, post-training, and inference — training disruption and denial-of-service, free-riding, model poisoning and backdoors, data extraction from gradients and activations, reputation and reward manipulation — and you keep that model current as the network grows. - Design and calibrate the tests: You build statistical verification methods with stated error rates, tune them with rigorous benchmarks, and keep false positives and false negatives controlled across heterogeneous hardware, including different GPUs and Macs. - Ship the verifier: You build and run the verification service in the inference path, and you live with its mistakes. ## WHAT WE'RE LOOKING FOR - Verification systems, shipped or published: You've built a calibrated statistical decision system with stated error rates and lived with its mistakes. Publications in inference and training verification count; fraud detection, anti-cheat, and experimentation platforms count as much as papers do. - Statistical depth: Deep expertise in statistics and probability, with the ability to design experiments, calibrate decision thresholds, and defend the error rates you claim. - Technical background: You know the solution space for verifying untrusted compute, from statistical testing to re-execution, cryptographic proofs, and trusted hardware, and you can argue what fits a permissionless network and what doesn't. - Mission alignment: You believe Protocol Learning is the viable third path for collective, trustless, and sovereign AI. ## NICE TO HAVE - Familiarity with large scale Pre-training and RL post-training. - Familiarity with decentralized ML security and adversarial threat models, such as poisoning, Sybil, collusion, and replay. - 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. ## 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 - [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 - 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 - [Research Scientist](https://feeny.ai/job/research-scientist-pluralis-research-usa-or-v7z1rqs3sas3) — Usa OR, Australia - [Founders Associate (Business Operations)](https://feeny.ai/job/founders-associate-business-operations-pluralis-research-sydney-z2b3q8c92pwz) — Sydney, Australia - [Events & Operations Associate](https://feeny.ai/job/events-operations-associate-pluralis-research-san-francisco-51ht4svfj5tx) — San Francisco, CA - [Academic Collaboration](https://feeny.ai/job/academic-collaboration-pluralis-research-sydney-kwpagzyta862) — Sydney, Australia