Principal Machine Learning Engineer

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 weve 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 were expanding the team scaling our systems and accelerating the application of deep learning in our research and execution workflows. Were looking for a Principal Machine Learning Engineer to help shape the next phase of our platform influencing architecture driving best practices and solving high-leverage problems. Youll 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 youve built ML infrastructure at scale elsewhere and are looking for a role where your ideas will genuinely help shape our firms future wed love to hear from you.

Your Core Responsibilities:

• 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
• Influence foundational choices around data access compute orchestration experiment tracking model versioning and deployment pipelines
• Partner with quant researchers to accelerate iteration cycles tighten feedback loops and bring models from prototype to live trading
• 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
• Shape our approach to reproducibility continual learning and production monitoring across a petabyte-scale data environment
• Define standards that create consistency across teams and geographies; mentor engineers and influence technical culture beyond your immediate work
• Keep pace with developments in deep learning research and ML infrastructure; bring ideas from academia and industry into how we work whether thats new architectures training techniques or tooling

Your Skills and Experience:

• 8 years of experience building ML platforms or infrastructure at a leading tech company research lab or quantitative firm
• A track record of designing and owning large-scale training and inference systems not just contributing but architecting
• Deep proficiency in Python with strong experience in either CUDA or C
• Hands-on expertise with modern deep learning frameworks (PyTorch TensorFlow or JAX) and practical experience implementing architectures like transformers attention mechanisms or sequence models
• Strong foundation in deep learning fundamentals: optimization regularization loss design and the trade-offs that matter when training at scale
• Experience with distributed training at scale (Horovod NCCL) and GPU optimization (cuDNN TensorRT)
• History of deploying models to production with strong observability reproducibility and monitoring practices
• Comfort working across the ML stack from data pipelines to training infrastructure to serving systems

Why This Role:

• Build dont inherit Youll make foundational technology choices in a platform thats still being defined not maintain someone elses legacy.
• Real investment real backing This is a strategic priority with resources behind it not a side experiment.
• Direct impact on trading Your infrastructure will power models that make real trading decisions in competitive global markets.
• Global scope Work with teams across New York Chicago Amsterdam London Sydney Hong Kong and beyond; define practices that can scale worldwide.
• Ideas over titles IMCs culture values clarity rigor and collaboration. The best ideas win regardless of where they come from.
• Tight coupling with research You wont be building in isolation. Researchers and engineers work side-by-side iterating together.

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