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 opportunity 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 Strong 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 strong 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
