We're looking for an early-career engineer to work directly with Pluralis Research Scientists and Machine Learning Engineers on core systems work. This is a hands-on IC role, ideal as a first or second job out of university. We will invest heavily in developing this person—the focus is on
raw potential and initiative
, not prior experience.
The role is broad: you'll work on distributed systems, run and monitor large-scale ML training (10B+ parameters), build infrastructure and experiment scaffolding, handle data engineering, and take on varied tasks that keep the research loop fast and efficient. You'll integrate published research into our systems, and have the prospect to contribute to research that will be published in top-tier conferences.
Responsibilities
Support and scale infrastructure for large ML training and research
Build tools and automations to reduce operational load on scientists
Take on varied engineering tasks across the stack as needed
Learn quickly, ship frequently, and grow into a core engineering contributor
Qualifications
Robust CS/math/EE fundamentals (e.g. first-class honours or equivalent)
Evidence of initiative: personal ML projects, hobbyist experiments, public repos, or other non-traditional work artifacts
High energy, self-directed, with solid learning velocity
Preferred
University Medal or equivalent academic achievement
Experience or internships at high-performance environments (quant firms, top-tier tech)
Active presence in ML communities (Discord, X, open-source, etc.)
Demonstrated ability to take projects from idea to working artifact independently
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