Pluralis Research works on Protocol Learning— training large models in a fully decentralised 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 training an 8B llama model from scratch with devices in physically different regions.
OverviewPluralis Research is building foundational systems for decentralised model training. As a Senior or Staff Frontend Engineer focused on Visualisation and UX, you’ll shape how users interact with distributed training systems — translating complex infrastructure into intuitive, high-fidelity interfaces.
You’ll work closely with research and product teams to design, build, and refine interactive tools that make large-scale model training legible, visual, and accessible.
ResponsibilitiesLead the development of frontend systems for interactive visualisation and control of distributed training environments.
Collaborate with researchers, designers, and engineers to translate technical workflows into user-centred experiences.
Build, test, and maintain high-performance frontend applications using modern frameworks.
Work with the Creative Lead to ensure cohesive design and implementation across UI systems.
Develop data visualisation layers for large-scale training metrics, model graphs, and cluster states.
Establish standards for frontend architecture, performance, and design-system integration.
Contribute to product design discussions, UX prototyping, and interface refinement.
Strong experience building complex, data-heavy web applications.
Deep understanding of modern frontend frameworks (React, TypeScript, or equivalent).
Proven ability to design and implement data visualisation or real-time monitoring tools.
Strong collaboration skills across design, research, and backend teams.
Familiarity with UX principles and accessibility standards.
Experience leading or owning large-scale frontend projects end-to-end.
Familiarity with observability/metrics ecosystems (Prometheus, Grafana)
Portfolio demonstrating strong design + engineering ability.
High-level understanding of modern ML (e.g., know what a “training loss” is)
Corporate/Standard Design and UX experience. For example - if you look at Google's material design v3 and think 'damn that's nice' - that would be bad.
