Principal Machine Learning Engineer

IMC is seeking a Principal Machine Learning Engineer to lead the development of its machine learning infrastructure. This role involves collaborating with researchers and technologists to design systems that support the experimentation, training, and deployment of machine learning models, thereby shaping the firm's future in trading technology.

Key responsibilities include designing and building end-to-end infrastructure for training, evaluating, and deploying machine learning models. The engineer will work closely with high-performance computing engineers to manage on-premises compute clusters, influence foundational choices around data access, compute orchestration, experiment tracking, model versioning, and deployment pipelines. Additionally, the role involves partnering with quantitative researchers to accelerate iteration cycles and bring models from prototype to live trading.

Candidates should have over eight years of experience building machine learning platforms or infrastructure at leading tech companies, research labs, or quantitative firms. A strong track record in designing and owning large-scale training and inference systems is essential. Deep proficiency in Python, with significant experience in CUDA or C++, is required. Hands-on expertise with modern deep learning frameworks such as PyTorch, TensorFlow, or JAX, and practical experience implementing architectures like transformers and sequence models are also necessary.

The base salary for this position ranges from $200,000 to $250,000 USD. Full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance.

IMC offers a collaborative culture that values clarity, rigor, and innovation. The firm provides opportunities to work with teams across global offices, define practices that can scale worldwide, and directly impact trading through the development of cutting-edge machine learning infrastructure.