Top Machine Learning Jobs in Chicago, IL

Leads product strategy and a product management team for enterprise AI/ML and machine learning platforms. Defines Generative AI and ML platform vision, evolves capabilities for data scientists and software developers, drives platform adoption, and partners with adjacent platform leaders on an integrated ecosystem. The role requires deep technical expertise in AI/ML infrastructure, Generative and Agentic AI, cloud and open-source technologies, people leadership, and AI risk management.

Leads the design, development, deployment, and maintenance of production machine learning systems at scale. Responsibilities include developing and validating models, building optimized data pipelines, writing application code, automating testing and deployment, monitoring and retraining production models, designing cloud-based ML architectures, and applying responsible AI and governance practices. The role collaborates with Product, Data Science, and Agile engineering teams and may include people leadership.

Leads Toast’s AI and analytics strategy for Enterprise Care. Owns the roadmap, evaluates and deploys AI-assisted support tools, develops operational intelligence products, and partners with Product, Engineering, and Data teams to move pilots into production. Establishes complexity and workload intelligence, assesses AI readiness, translates AI performance into measurable operational outcomes, and builds organizational AI literacy. The role requires extensive enterprise support or service-delivery leadership, hands-on production AI experience, operational data fluency, and the ability to drive trusted adoption among agents and executives.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Own the ML platform powering production training pipelines, model serving, deployment, orchestration, observability, and multi-region scaling. Drive major infrastructure initiatives, improve reliability and cost efficiency, lead incident response, collaborate across teams, and set the technical vision for ML operations. The role is hands-on, requiring production distributed-systems expertise, Kubernetes and cloud infrastructure knowledge, technical leadership, mentoring, and strong communication.

Lead Data Scientist partners with business leaders to identify opportunities, design, develop, and deploy advanced analytics and ML solutions. Responsibilities include data preparation, modeling, feature engineering, delivering insights and visualizations, establishing standards, and directing associate data scientists, contractors, and consultants to produce measurable business value.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Design, build, and deploy AI and machine learning solutions at scale. Develop data pipelines and scalable models, perform data wrangling and analysis, apply NLP and neural networks, collaborate with clients and teams, and contribute to open-source and iterative improvement of AI systems.

Leads product vision and strategy for generative AI use cases across business functions. Defines requirements, prioritizes roadmaps, conducts market and competitor research, and presents business plans to leadership. Collaborates with customers, engineers, designers, data scientists, and stakeholders to prototype and develop AI solutions. Recommends technology architectures and supports emerging technology initiatives, workforce transformation, and strategic technology programs.

Develop and implement statistical and machine learning trading strategies and quantitative models. Design optimization algorithms for portfolio construction and explore new research opportunities within derivatives and market behavior.

Develop and own end-to-end machine learning models for Uber Freight’s marketplace, including cost prediction, booking probability, demand elasticity, pricing, matching, recommendations, and optimization. Partner with backend engineers to deploy models to production and ensure reliable performance. Apply predictive modeling, causal inference, constrained optimization, reinforcement learning, and marketplace design to solve complex logistics problems.

Designs, builds, and operationalizes secure, scalable generative AI and machine learning systems across AWS and GCP. Responsibilities include developing LLM, agentic, and RAG applications; architecting serverless, event-driven microservices; managing model experimentation, evaluation, deployment, and monitoring; and optimizing performance and cost. The role also leads responsible AI, HIPAA compliance, governance, and technical collaboration while mentoring engineers and managing team performance.

Principal-level senior individual contributor and strategic co-leader for Financial Services Consulting Data Science and Analytics. Drives strategy, operating model design, revenue and capacity planning, analytics consulting delivery, AI and machine learning solution development, product commercialization, data governance, platform stewardship, thought leadership, and talent development. Guides production-grade model pipelines, supports client engagements and sales, and ensures scalable, compliant analytics outcomes across a matrixed organization.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

Own the end-to-end architecture, development, deployment, evaluation, and operations of AI and machine learning systems. Build predictive ordering models, agentic workflow copilots, retrieval and evaluation infrastructure, safety guardrails, and responsible AI practices. Prioritize AI initiatives based on business outcomes, establish model governance and production standards, advise product and engineering leadership, and mentor experienced contributors.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

Develop a predictive supply chain risk assessment tool using internal supply metrics and external market data. Build and validate machine learning models for risk scoring, time-series forecasting, and anomaly detection. Create dashboards in Power BI or Tableau, engineer market-related features, integrate data pipelines, document model performance, and communicate actionable insights to supply chain planners and stakeholders.

Leads client-facing data science and analytics initiatives, translating complex marketing and media data into actionable business narratives. Develops measurement strategies, including test/holdout incrementality, informs acquisition and retention decisions, scales analytics best practices, and manages analyst teams. Partners with Sales, Client Services, Operations, and other stakeholders while supporting predictive and adaptive AI/ML solutions. The role requires strong client communication, strategic leadership, analytics expertise, and proficiency with Python, SQL, Tableau, and related tools.

Architect and lead development of scalable AI and cloud-native agentic solutions and AI transcription platforms using OpenAI, Azure, and AWS. Design, build, and maintain high-performance distributed systems; provide technical leadership, enforce engineering best practices, lead DevOps (Docker, Kubernetes, AWS Fargate) and CI/CD with SonarQube, optimize infrastructure costs, and collaborate cross-functionally to align technical solutions with financial business needs.

Lead a product team building ML and GenAI infrastructure and model delivery for marketing and acquisitions. Define problem statements, set 3-year roadmaps tied to business outcomes, partner with data scientists and engineers, drive reusable scalable capabilities, influence cross-functional stakeholders, recruit and mentor talent, and measure success with data-driven metrics.

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.

Ten-week, full-time paid Data Science internship for current PhD students. Interns will build and evaluate modeling and analytics tools, integrate internal and external data sources, design data science models, and communicate results. Candidates must continue their PhD after the internship and may be evaluated for full-time roles upon graduation.

The role involves deploying and supporting CrowdStrike's AI capabilities in cybersecurity, optimizing threat detection, and mentoring teams on AI solutions.

Develop, evaluate, and deploy machine learning models for real trading environments; apply rigorous statistical techniques to measure performance, control overfitting, and assess model risk and interpretability; collaborate with traders and developers; maintain best practices and production-ready systems.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.