Principal Machine Learning Engineer

<p>At IMC, we believe technology is the foundation of our competitive edge — and machine learning is increasingly central to how we trade. Over the past few years, we've been steadily building our machine learning capabilities: developing infrastructure, growing our in-house GPU cluster, deploying models into production, and partnering closely with quant researchers and traders to generate real impact. Now we’re expanding the team, scaling our systems, and accelerating the application of deep learning in our research and execution workflows.  We're looking for a <strong>Principal Machine Learning Engineer</strong> to help shape the next phase of our platform — influencing architecture, driving best practices, and solving high-leverage problems. You’ll work alongside researchers and technologists to design the systems that power experimentation, training, and deployment of ML models — and help set the direction for how machine learning is done at IMC as we scale. If you’ve built ML infrastructure at scale elsewhere and are looking for a role where your ideas will genuinely help shape our firm’s future — we’d love to hear from you.</p> <p><strong>Your Core Responsibilities: </strong></p> <ul> <li>Design and build end-to-end infrastructure for training, evaluation, and productionization of ML models, working closely with our HPC engineers who manage our on-prem compute cluster</li> <li>Influence foundational choices around data access, compute orchestration, experiment tracking, model versioning, and deployment pipelines</li> <li>Partner with quant researchers to accelerate iteration cycles, tighten feedback loops, and bring models from prototype to live trading</li> <li>Work with researchers to adapt and deploy modern architectures — transformers, state-space models, temporal convolutions, graph neural networks — to noisy, high-frequency financial data. Explore techniques like self-supervised pretraining, representation learning, and cross-sectional modelling where they offer genuine edge</li> <li>Shape our approach to reproducibility, continual learning, and production monitoring across a petabyte-scale data environment</li> <li>Define standards that create consistency across teams and geographies; mentor engineers and influence technical culture beyond your immediate work</li> <li>Keep pace with developments in deep learning research and ML infrastructure; bring ideas from academia and industry into how we work — whether that's new architectures, training techniques, or tooling</li> </ul> <p><strong>Your Skills and Experience: </strong></p> <ul> <li>8+ years of experience building ML platforms or infrastructure at a leading tech company, research lab, or quantitative firm</li> <li>A track record of designing and owning