Top Senior Machine Learning Jobs in Chicago, IL

Lead the development of fraud prediction models using machine learning, collaborating across teams to build, scale, and monitor models in production.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

Lead the SDL and Cash Line ML teams, developing innovative machine learning models to improve lending solutions and expand credit access. Collaborate with cross-functional teams to drive product strategy and enhance model performance, while mentoring a team of ML scientists.

As a Principal Applied Scientist, you'll define the technical vision for offer optimization and conversion modeling systems, ensuring alignment across teams while overseeing large technical initiatives and contributing to real-world machine learning solutions.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Manage a team of ML engineers to build and optimize Affirm's economic decisioning engine using advanced ML techniques. Collaborate across teams, provide technical leadership, and mentor engineers while driving a cohesive long-term ML strategy.

Responsible for validating payment network models, including fraud and AML. Engage with stakeholders, improve model accuracy, and leverage innovative technology for data analysis.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

The Machine Learning Engineer will develop and optimize large-scale ML models, build low-latency inference pipelines, and collaborate with teams to enhance performance and automate ML processes.

This role involves developing ML tools and frameworks, supporting data science environments, and leading projects from design to execution. Candidates need strong programming skills and a quantitative background.

Build and deploy transformer and sequential deep learning models using large-scale financial and behavioral data to power personalized growth and marketing experiences. Work cross-functionally to design scalable training, serving, and monitoring infrastructure (batch and real-time), contribute to experimentation and optimization frameworks, and maintain production-grade MLOps systems to improve member engagement and business metrics.

The Senior Manager for Machine Learning will lead the ML team, coordinate initiatives, and enhance product development in a collaborative environment.

Lead design and implementation of a scalable data lakehouse and analytical layer in Google Cloud, develop and deploy machine learning models and pipelines, mentor data scientists, set technical strategy, and partner with stakeholders to drive predictive formulation and product innovation.

The role involves designing and deploying strategies for congestion revenue rights, applying quantitative methods, and developing machine learning models.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

Design and build machine learning systems for Coinbase, responsibly use generative AI tools and copilots, apply human-in-the-loop practices, and deliver measurable efficiency, cost, and quality improvements while collaborating in a remote-first environment with periodic in-person surges.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

Lead development and deployment of fraud and risk models using ML/AI. Build scalable data pipelines, ensure data quality, validate models, collaborate with engineering and product teams, mentor junior data scientists, and monitor fraud/risk metrics to optimize detection while balancing user experience.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

As a Senior Machine Learning Engineer, you will architect and build scalable ML infrastructure, influence technical direction, mentor engineers, and ensure effective production deployment, collaborating closely with AI teams.

Lead ML data and feature strategy for fraud detection, own end-to-end feature engineering pipelines, close tooling gaps and drive infrastructure roadmap, partner with ML engineers to productionize models, mentor junior staff, collaborate with product and risk teams, and serve as the team's institutional expert on ML evolution for ATO and scam prevention.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.