Machine Learning Engineer

Title: Machine Learning Engineer Location: New York, United States

Machine Learning Engineers at IMC play a pivotal role in building the systems that drive training and deployment of large-scale ML models across IMC's global operations. They collaborate with quantitative researchers, hardware specialists and software engineers to build robust systems that get the most out of GPU acceleration, distributed computing and the broader open-source ML ecosystem. ML Engineers' work directly shapes how fast we can experiment, iterate and deploy — accelerating the cycle that turns a research idea into a live trading strategy.

This is a unique opportunity to solve problems at the intersection of advanced machine learning and trading, where your contributions will shape the future of IMC's technology and trading capabilities.

As a team, our Machine Learning Engineers broadly work across two areas: ML Research Engineering, focused on the systems and frameworks researchers use to train, evaluate and iterate on models at scale; and ML Performance Engineering, focused on making ML workloads execute as efficiently as possible, typically through GPU and low-level systems optimization. These aren't rigid tracks, and there is overlap between them. You will bring depth of expertise in at least one of these focus areas and collaborate closely with global team members.

Your Core Responsibilities

The responsibilities below are representative of the role — the exact mix depends on your team, area of expertise and location.

ML Research Engineering

• Develop large-scale distributed training pipelines that manage complex datasets and models.
• Partner with quantitative researchers to automate ML experiments, hyperparameter tuning and model retraining.
• Work with HPC specialists to optimise workflows, improve training speed and reduce compute cost.

ML Performance Engineering

• Build and optimise low-latency inference pipelines, ensuring models deliver real-time predictions in production systems.
• Maximise performance in training and inference using GPU hardware and acceleration libraries.

Across both areas

• Extend and improve the internals of machine learning frameworks and open-source tools to raise their performance for our workloads.
• Design scalable model infrastructure capable of handling high-volume trading data and delivering real-time, high-accuracy predictions.
• Evaluate and roll out third-party tools that enhance model development, training and inference capabilities.

Your Skills and Experience

Core, regardless of focus area:

• 3+ years of 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.
• 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.
• A systems mindset — you reach for measurement before opinion on performance, scale or reliability issues, and can make sound build-versus-buy decisions.
• 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.

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.
• 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).
• Production inference systems — hands-on experience building and hardening real-time, low-latency prediction pipelines in high-performance environments.
• Numerics and correctness at scale — low-precision effects, KV caching, batching, and knowing which optimisations stay bit-exact.

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 Deep Learning Researcher roles are likely a closer fit. If your strength is in general infrastructure rather than ML-specific systems, take a look at our broader infrastructure engineering roles.

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