Machine Learning Engineer - Intern

Pluralis Research Ltd

Job Overview

Location

Melbourne

Job Type

Full-time

Full Job Description

📋 Description

• Design, build and ship a well-scoped project on the Agora roadmap, with a final presentation to both the engineering and research teams.
• Build and improve concurrent and parallel systems components (multiprocessing, async I/O and threading) within a production distributed training stack.
• Work hands-on with large-scale training infrastructure across cloud providers (AWS/GCP).
• Contribute to the production codebase daily: code review and mentorship from a buddy on the Agora team.

🎯 Requirements

• Current enrolment in (or recent completion of) a Masters or PhD in machine learning, computer science or a related field.
• A genuine ML background.
• Strong Python and PyTorch.
• Experience building concurrent or parallel systems (multiprocessing, async I/O, threading).
• Hands-on exposure to distributed machine learning, via internship, coursework or serious projects.
• Experience working with AWS, GCP or other hyperscalers.

🏖️ Benefits

• Opportunity to work on a world-class, deeply technical team of ML researchers.
• Backed by Union Square Ventures and other tier-1 investors.
• Ability to implement a genuinely open, collaborative path to frontier-scale AI.
• Opportunity to work on a production decentralised training system.

Skills & Technologies

Python

Node.js

AWS

GCP

PyTorch

Junior

Remote

Degree Required

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Pluralis Research Ltd

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About Pluralis Research Ltd

Pluralis Research develops a novel approach to training large AI models called “Protocol Learning.” Instead of traditional centralized or open-source models, their method enables decentralized, multi-participant model training where no single party ever holds a full copy of the model weights. This makes models “unextractable” and supports collaborative ownership, allowing value from model usage to flow back to contributors. They aim to democratize access and innovation in AI, reduce dependency on large tech firms, and create a sustainable, open ecosystem for foundation model development.

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