Top Fintech Machine Learning Engineer Jobs in Chicago, IL

Develop, validate, and deploy machine learning models and scalable ML pipelines. Preprocess and analyze data, run experiments, collaborate with senior engineers and stakeholders, and support model risk governance and regulatory-compliant AI applications (fraud detection, credit scoring, marketing analytics).

The Applied AI Data Scientist will create and optimize ML and GenAI models, design pipelines, run experimental evaluations, and integrate solutions across cloud platforms while collaborating with teams to drive business impact.

Lead and grow Matter's AI and data science efforts to build personalized meal-planning models. Develop ML and reinforcement learning algorithms, integrate LLM conversational interfaces, design feedback loops and dashboards, collaborate cross-functionally, and ensure alignment with nutrition science and ethical AI practices.

The Senior Machine Learning Engineer will manage the lifecycle of ML models, transform prototypes to production code, build training pipelines, and monitor system performance.

Lead end-to-end research applying advanced ML/DL (transformers, GNNs, RL, ensembles) to generate alpha across digital asset markets, build production-grade pipelines, prevent leakage/overfitting, analyze microstructure and on-chain data, and mentor junior researchers.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Develop, implement, and maintain ALM and CCAR/PPNR models for balance sheet forecasting. Build customer behavior, prepayment, and deposit models; monitor performance, perform backtesting and sensitivity analysis; collaborate with Model Risk, Market Risk, Treasury, and validation teams; prepare documentation and materials for governance and senior leadership; support balance sheet optimization and regulatory initiatives.

Develop, train, test, and integrate machine learning models; process large datasets; optimize AI systems; collaborate with cross-functional teams; document architectures; ensure ethical, performant AI solutions using cloud services.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

As an Applied AI Engineer, you will design and implement ML systems for customer interactions, focusing on performance and scalability.