IMC - Machine Learning Engineer
Requirements
• Core, regardless of focus area: • 3+ years' experience in machine learning, with a focus on training or inference systems. • Production software engineering — Python, testing discipline and genuine ownership of systems once they ship. • Production software engineering — • Machine learning fundamentals — you understand modern ML workloads beyond high-level APIs and can reason about model behaviour, correctness and performance at the level relevant to your area of expertise. • Machine learning fundamentals — • A systems mindset — you reach for measurement before opinion on performance, scale or reliability issues, and can make sound build-versus-buy decisions. • A systems mindset — • Strong collaboration — comfortable explaining the reasoning behind your technical decisions, working through differing perspectives with researchers and adapting your approach when the evidence supports it. • Strong collaboration — • Depth in one or more of the following: • Distributed training and large-scale systems — parallelism strategy, data pipelines and checkpointing; frameworks such as PyTorch, TensorFlow or JAX. • Distributed training and large-scale systems — • GPU performance and low-level optimisation — reasoning about memory, compute and latency at the device level, and optimising performance across kernels, compilers and hardware (e.g. CUDA, Triton or equivalent). • GPU performance and low-level optimisation — • Production inference systems — hands-on experience building and hardening real-time, low-latency prediction pipelines in high-performance environments. • Production inference systems — • Numerics and correctness at scale — low-precision effects, KV caching, batching, and knowing which optimisations stay bit-exact. • Numerics and correctness at scale — • Technologies listed above are examples rather than requirements; equivalent depth developed using other stacks or hardware platforms is equally relevant. • Also a plus: • Exposure to cloud platforms and orchestration tools. • A track record of contributing to open-source projects in machine learning, data science or distributed systems. • Not sure this is the one? If your strength is mainly in modelling and research direction, our QR / Modeller roles are likely a closer fit. • Not sure this is the one? • IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.
Responsibilities
• The opportunity to be in a brand new position in a growing team • Develop large-scale distributed training pipelines to manage datasets and complex models • Build and optimize low-latency inference pipelines, ensuring models deliver real-time predictions in production systems • Develop libraries to improve the performance of machine learning frameworks • Maximize performance in training and inference using GPU hardware and acceleration libraries • Design scalable model frameworks capable of handling high-volume trading data and delivering real-time, high-accuracy predictions • Collaborate with quantitative researchers to automate ML experiments, hyperparameter tuning, and model retraining • Partner with HPC specialists to optimize workflows, improve training speed, and reduce costs • Evaluate and roll out third-party tools to enhance model development, training, and inference capabilities • Dig into the internals of open-source ML tools to extend their capabilities and improve performance
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