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Machine Learning Engineer - Intern

Role overview

Qualifications

  • 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).

Responsibilities

  • 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.

About the company

Pluralis Research logo

Pluralis Research

Artificial Intelligence & Machine Learning Services

Pluralis is developing a protocol that facilitates collaborative training and ownership of foundation models.

Company details

IndustryArtificial Intelligence & Machine Learning Services
Company size1 - 10

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Job description

Pluralis Research is pioneering Protocol Learning - a fully decentralised way to train and deploy AI models that opens this layer to individuals rather than well resourced corporates. By pooling compute from many participants, incentivising their efforts, and preventing any single party from controlling a model's full weights, we're creating a genuinely open, collaborative path to frontier-scale AI.

As a Machine Learning Engineer Intern, you'll work on Agora - our production decentralised training system - alongside the engineers running real multi-node training at scale. This is a fixed-term internship (3 months, with option to extend). 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

  • 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

What We're Looking For

  • Current enrolment in (or recent completion of) a Masters or PhD in machine learning, computer science or a related field. Engineering-inclined candidates from either level are welcome.

  • A genuine ML background. You can keep pace with a research-driven team, not just a strong generalist engineering profile.

  • 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.

  • Australia-based.

Nice to Have

  • Top-tier ML publications (welcome, but not required for systems-focussed candidates).

  • Open-source contributions to ML frameworks, distributed systems or networking libraries.

FYI's

  • This internship is for Australia-based candidates only.

  • Applicants must have professional-level English proficiency (written and spoken).

  • Pluralis is a remote team across Australia and the US. You'll need to be comfortable working across timezones and collaborating with a diverse, distributed group.

  • Recruiters: we aren't looking for agency support at this time. We'll reach out if we need help.

Backed by Union Square Ventures and other tier-1 investors, we're a world-class, deeply technical team of ML researchers. Pluralis is unapologetically ideological. We view the world as a better place if we are able to implement what we are attempting, and Protocol Learning as the only plausible approach to preventing a handful of massive corporations monopolising model development, access and release, and achieving massive economic capture. If this resonates, please apply.

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MR

Marcus Rivera

Chief Revenue Officer

m.rivera@company.com
linkedin.com/in/marcusrivera
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