Machine Learning Engineer Jobs in Chicago, USA

<p>As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the training and deployment of large-scale ML models across our global operations. You'll collaborate with leading researchers, hardware experts, and software engineers to build robust solutions that maximize the potential of GPU acceleration, distributed computing, and the latest open-source tools. Your work will influence our trading strategies by accelerating experimentation cycles that foster continuous innovation and refinement.</p> <p>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.</p> <p><strong>Your Core Responsibilities: </strong></p> <ul> <li>Develop large-scale distributed training pipelines to manage datasets and complex models</li> <li>Build and optimize low-latency inference pipelines, ensuring models deliver real-time predictions in production systems</li> <li>Develop libraries to improve the performance of machine learning frameworks</li> <li>Maximize performance in training and inference using GPU hardware and acceleration libraries</li> <li>Design scalable model frameworks capable of handling high-volume trading data and delivering real-time, high-accuracy predictions</li> <li>Collaborate with quantitative researchers to automate ML experiments, hyperparameter tuning, and model retraining</li> <li>Partner with HPC specialists to optimize workflows, improve training speed, and reduce costs</li> <li>Evaluate and roll out third-party tools to enhance model development, training, and inference capabilities</li> <li>Dig into the internals of open-source ML tools to extend their capabilities and improve performance</li> </ul> <p><strong>Your Skills and Experience: </strong></p> <ul> <li>5+ years of experience in machine learning with a focus on training or inference systems</li> <li>Hands-on experience with real-time, low-latency ML pipelines in high-performance environments is a strong plus</li> <li>Strong engineering skills, including Python, CUDA, or C++</li> <li>Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX</li> <li>Proficiency in GPU programming for training and inference acceleration (e.g., CuDNN, TensorRT)</li> <li>Experience with distributed training for scaling ML workloads (e.g., Horovod, NCCL)</li> <li>Exposure to cloud platforms and orchestration tools</li> <li>A track record of contributing to open-source projects in machine learning, data science, or distributed systems is a plus</li> </ul> <p><span style="color: rgb

<p>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, we've 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 we’re expanding the team, scaling our systems, and accelerating the application of deep learning in our research and execution workflows.  We're looking for a <strong>Principal Machine Learning Engineer</strong> to help shape the next phase of our platform — influencing architecture, driving best practices, and solving high-leverage problems. You’ll 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 you’ve built ML infrastructure at scale elsewhere and are looking for a role where your ideas will genuinely help shape our firm’s future — we’d love to hear from you.</p> <p><strong>Your Core Responsibilities: </strong></p> <ul> <li>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</li> <li>Influence foundational choices around data access, compute orchestration, experiment tracking, model versioning, and deployment pipelines</li> <li>Partner with quant researchers to accelerate iteration cycles, tighten feedback loops, and bring models from prototype to live trading</li> <li>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</li> <li>Shape our approach to reproducibility, continual learning, and production monitoring across a petabyte-scale data environment</li> <li>Define standards that create consistency across teams and geographies; mentor engineers and influence technical culture beyond your immediate work</li> <li>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</li> </ul> <p><strong>Your Skills and Experience: </strong></p> <ul> <li>8+ years of experience building ML platforms or infrastructure at a leading tech company, research lab, or quantitative firm</li> <li>A track record of designing and owning

<p data-renderer-start-pos="14" data-local-id="7c4a07c6b9ef">We are deploying machine learning directly onto custom hardware – and we want you to help drive it from the ground up. This is an initiative where you'll have the rare opportunity to architect solutions from scratch, influence technical research direction, and see your work drive real impact in one of the most demanding computing environments in the world.</p> <p data-renderer-start-pos="374" data-local-id="a232a52966f2">We build the hardware, the software, and the infrastructure, so when you hit a bottleneck, you can fix it - there's no vendor to wait on and no abstraction layer you're not allowed to touch. If you've ever wanted to push the boundaries of what's computationally possible, this role is for you. We're looking for researchers and experienced engineers from any background. Trading experience is a bonus, not a prerequisite.</p> <p><strong>Your Core Responsibilities</strong></p> <ul> <li data-renderer-start-pos="827" data-local-id="3d8f27672cb3">Architect and co-design ML models with traders, quant researchers, and software engineers, treating hardware constraints (latency budgets, resource limits, numerical precision) as first-class design inputs</li> <li data-renderer-start-pos="1036" data-local-id="e4081dba3255">Shape our custom hardware roadmap by translating ML model requirements into concrete architectural decisions</li> <li data-renderer-start-pos="1148" data-local-id="2adc2165f97e">Work hands-on with hardware engineers to implement, verify, and deploy ML inference solutions from proof-of-concept through production</li> <li data-renderer-start-pos="1286" data-local-id="a39313dd0463">Track and evaluate emerging research in neural architecture search, machine learning systems and quantization methods, and determine what translates to measurable improvements in our systems</li> </ul> <p><strong>Your Skills and Experience</strong></p> <ul> <li data-renderer-start-pos="1510" data-local-id="f9874e7528e2">Solid understanding of hardware constraints and design trade-offs (e.g., pipelining, resource utilization, fixed-point arithmetic) that shape how ML models can be efficiently mapped onto FPGAs or custom ASICs</li> <li data-renderer-start-pos="1722" data-local-id="d265ff5d3d92">Experience with hardware fundamentals, whether through <span data-highlighted="true" data-vc="highlighted-text"><span class="_kqswh2mm"><span class="_5pioz8co _189e1dm9 _1il9buyh _19lc184f _d0altlke" data-testid="definition-highlighter">VHDL</span></span></span>/SystemVerilog developme