Top Senior Machine Learning Jobs in Chicago, IL

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 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.

Build, deploy, and monitor machine learning and pattern-recognition models to detect lending fraud. Analyze large transactional datasets with Python and SQL, partner with Fraud Operations to iterate on features and reduce false positives/negatives, communicate findings to stakeholders, apply AI in production, and mentor team members.

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.

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

Design, develop, and deploy scalable AI/ML solutions and agentic LLM systems for payments. Build data pipelines, perform analysis, partner with Product, Technology, Risk and Compliance, track model performance and regulatory documentation, and translate model outcomes into business impact.

Lead design and delivery of scalable Amazon Connect voice and chat solutions. Build Lambda-based integrations, integrate Lex bots and external systems, optimize routing/queues, ensure high availability and monitoring, lead production support, RCA, and continuous improvement for contact center platforms.

Lead product efforts for machine learning-driven marketing and acquisition products. Collaborate with data scientists, engineers, and designers to define acceptance criteria, break down epics into MVP features and user stories, prioritize roadmaps, manage agile delivery, and drive stakeholder alignment to build scalable ML product platforms.

Lead end-to-end analytics and ML initiatives for PBM member engagement: build, deploy, and monitor large-scale ML/DL models; perform EDA and data quality assessments on prescription claims; collaborate with engineering and stakeholders to create feature stores and MLOps pipelines; communicate results to business leaders; mentor peers and interns.

Lead a small team to design, build, deploy, and monitor ML/AI solutions for Claims (fraud, triage, severity, litigation, subrogation). Manage projects, collaborate with engineering and IT, follow model governance, and mentor staff.

Lead the AI & Analytics stream to design, build, and deploy predictive and prescriptive ML models, integrate analytics into enterprise platforms (Salesforce, ServiceNow), automate root-cause and log analysis, translate stakeholder needs into production solutions, and manage analytics projects end-to-end while communicating results to technical and executive partners.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Design and maintain data models and pipelines for mortgage and lending platforms. Develop ML/AI, predictive and prescriptive analytics, and LLM/Generative AI solutions. Collaborate with engineering and business stakeholders to build scalable cloud and data warehouse solutions, ensure data quality and governance, and deliver dashboards and executive reporting.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Lead integration of state-of-the-art AI/ML into Netflixs content platform and publishing tools. Build roadmaps, incubate ML systems from idea to production, partner with researchers and engineers, automate promotional asset creation, optimize distribution and localization, and measure impact with data-driven evaluation.

The Senior Machine Learning Engineer leads technical direction for ML systems focused on personalization and ranking, overseeing architecture and implementation while mentoring team members.

Build, ship, and own end-to-end ML models for vendor matching, order routing, and supply-chain optimization. Partner with data engineering to productionize models, quantify impact, document work, and implement decision logic components toward agentic AI in a fast-moving healthcare startup environment.

Lead causal inference and measurement efforts for payments, build and evaluate ML/AI and LLM-based models, optimize fraud mitigation and loss strategies, simulate interventions, and communicate findings to stakeholders to drive product and risk decisions.

Lead a multidisciplinary team to design, deploy, and optimize ML-driven bidding systems and measurement for a nine-figure marketing budget. Drive experimentation, partner strategy, media-mix optimization, and end-to-end measurement while aligning Marketing, Analytics, Engineering, and Product to improve marketing ROI and scale performance.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.