IMC is looking to expand its machine learning capabilities, scaling its systems and accelerating the application of deep learning in research and execution workflows to gain a competitive edge in trading.
Requirements
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
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
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 that's new architectures, training techniques, or tooling
Other
Bachelor's, Master's, or Ph.D. degree in a relevant field (not explicitly mentioned but implied)
Ability to work across the ML stack from data pipelines to training infrastructure to serving systems
Collaboration and communication skills to work with researchers, engineers, and traders
Ability to work in a global team with offices in the US, Europe, Asia Pacific, and India
Commitment to giving back and contributing to the company's culture
