Research Engineer Intern
Posted 31 Aug 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Full-time
No salary listed for this role.
Field of studyComputer Science, Machine learning (or related)
Study levelMaster's or higher
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.
As a Research Engineer Intern you'll work on Agora, our production decentralized training system, alongside the engineers running real multi-node training at scale. Intern projects are well-scoped pieces of our live roadmap, not side experiments: you'll ship real work in week one and own a genuine open problem by the end of your internship.
Key Responsibilities
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Ship a roadmap project: Design, build, and ship a well-scoped project on the Agora roadmap, with a final presentation to the engineering and research teams.
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Concurrent and parallel systems: Build and improve multiprocessing, async I/O, and threading components inside a production distributed training stack.
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Training infrastructure: Work hands-on with large-scale training infrastructure across cloud providers (AWS/GCP).
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Daily production work: Contribute to the production codebase every day, with code review and mentorship from a buddy on the Agora team.
What We're Looking For
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ML background (required): Current enrollment in, or recent completion of, a Masters or PhD in machine learning, computer science, or a related field. You can keep pace with a research-driven team, not just a strong generalist engineering profile.
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Strong engineering: Strong Python and PyTorch, and experience building concurrent or parallel systems: multiprocessing, async I/O, threading.
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Distributed ML exposure: Hands-on exposure to distributed machine learning via internship, coursework, or serious projects, and experience with AWS, GCP, or other hyperscalers.
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Mission alignment: You believe Protocol Learning is the viable third path for collective, trustless, and sovereign AI.