Top Machine Learning Jobs in Chicago, IL

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Design, build, deploy, and support production AI applications for complex enterprise workflows. Develop LLM, retrieval, agentic, API, and distributed-service capabilities; create evaluation, testing, tracing, and observability practices; investigate AI failures; and optimize quality, security, performance, maintainability, and cost. Collaborate with domain experts, architects, data teams, and software engineers to translate specialized knowledge into reliable, explainable, and maintainable solutions.

Lead real-world evidence studies for pharmaceutical clients using complex clinical, molecular, claims, EHR, and registry data. Design observational analyses, derive endpoints, apply survival analysis and causal inference methods, interpret results, and communicate findings to technical and non-technical stakeholders. Develop reusable analytical code and standards while incorporating LLMs and agentic workflows into research processes. Collaborate cross-functionally to improve data quality, products, and RWE methodologies, with a focus on oncology and clinical development.

Build, test, deploy, and evaluate machine learning models using established frameworks. Support feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborate with engineering and product teams to define AI requirements, research performance improvements, and respond to critical IT incidents. The role requires advanced quantitative education, 5+ years of AI/ML engineering experience, Python expertise, and knowledge of deep learning and generative AI.

Leads the data science function by managing and coaching data scientists, developing analytics methodologies, partnering with business leaders, and delivering predictive and prescriptive models. Drives adoption of analytical insights, educates stakeholders, manages vendors, recruits talent, and oversees team performance. Requires advanced expertise in statistics, machine learning, Python, R, SQL, Databricks, distributed computing, and cloud analytics.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Senior Data Scientist partnering with Product and Customer Support Operations to improve marketplace decisions and performance. Responsibilities include exploratory analysis, root-cause investigations, experimentation, causal analysis, production machine learning model ownership, metric and dashboard development, self-service reporting, and LLM-assisted analysis. The role requires strong Python and SQL skills, BI tool experience, applied machine learning expertise, and the ability to influence product and operational roadmaps.

Own strategy and roadmap for Crunchbase's customer-facing AI data layer. Identify and validate model-derived data products, lead cross-functional development, set quality standards, drive adoption and monetization, and monitor performance and product economics.

The AI Engineer designs, develops, and operationalizes responsible AI systems, including generative AI applications, RLHF models, guardrails, prompt engineering, and RAG frameworks. Responsibilities include evaluating and fine-tuning models, developing Python applications on cloud platforms, implementing ML software engineering practices and CI/CD pipelines, and collaborating with stakeholders on AI roadmaps and delivery. The role requires leading complex AI/ML initiatives and translating technical concepts for diverse audiences.

Develop and validate machine learning and predictive models, perform statistical and data analysis, conduct ETL across repositories, and assess model quality. Communate findings through visualizations and written reports, automate analytical workflows, and collaborate directly with nontechnical customers to refine requirements and deliver data-driven solutions. The role requires expertise in ML/AI, programming, cloud environments, CI/CD, and government or safety-critical systems.

Leads OuterBox’s internal AI strategy, AI engineering team, and development of client-facing AI services. Identifies market opportunities, converts internal capabilities into priced consulting offerings, oversees production deployments and partner programs, and drives AI adoption, training, and measurement. Partners with executives, clients, engineering, and technology teams on architecture, security, cloud governance, reliability, pricing, and ROI.

Own analytical relationships with travel technology partners, creating scalable reporting on supply, pricing, and performance. Conduct exploratory analysis, apply machine learning and statistical methods, build dashboards, automate recurring workflows with AI tools, and translate complex findings into recommendations for product, engineering, commercial, and executive audiences.

Build and deploy AI-enabled solutions for Marketplace operations and commercial challenges. Identify opportunities, frame ambiguous business problems, develop prototypes using enterprise data and AI/LLM tools, validate business value, and transition successful solutions into production. Partner with business, product, technology, and data teams; drive adoption, measure outcomes, coordinate initiatives, mentor team members, and enhance reusable platforms and frameworks.

Build and deploy machine learning models for risk and fraud detection. Lead technical efforts in the risk and fraud domain, use SQL and Python for data preparation and production code, design statistically sound experiments, evaluate model performance, and partner with Operations to respond to evolving financial threats.

Designs, builds, and evaluates complete AI systems for Pearson’s K-12 assessment platform. Responsibilities include predictive machine learning, statistical methods, retrieval, knowledge graphs, generative and agentic AI, deterministic services, human review, and system integration. The role develops evaluation datasets and regression suites, analyzes failure modes, supports model adaptation, monitors deployed systems, and collaborates with engineers, psychometricians, learning scientists, and content experts to create transparent, reliable, and auditable AI capabilities.

Researches and develops AI, machine learning, computer vision, signal processing, and control algorithms for autonomous defense and commercial systems. Builds, trains, evaluates, and optimizes models; develops prototypes and data products; defines requirements; and collaborates across perception, mission planning, simulation, testing, and customer application teams.

Develops data visualization, analytics, machine learning, and automation solutions for coherent optical product manufacturing. Mines wafer-to-module datasets to identify yield drivers, failure signatures, anomalies, correlations, and process drift. Builds and evaluates ML algorithms for yield prediction and root-cause analysis, performs statistical analysis, and automates engineering workflows using GenAI. Collaborates with R&D, manufacturing, and AI/ML teams to optimize product performance and communicate findings to technical audiences.

Researches, designs, develops, trains, and deploys computer vision and machine learning models. Builds and maintains ML software, testing and validation processes, and documentation. Applies image and video processing, deep learning, and computer vision techniques across edge and cloud-based platforms while improving existing applications and performance.

Applied data scientist responsible for solving data-quality and machine-learning problems, evaluating model performance in production, detecting drift, building ground truth, and prototyping data products. The role uses classification, entity resolution, anomaly detection, embeddings, semantic search, feature engineering, statistical analysis, dashboards, and large-scale production data. The scientist works independently within product or data teams and partners with engineers, analysts, product managers, and subject-matter experts.

Leads the team building LTK’s foundational AI platform, including shared models, services, evaluation frameworks, and production LLM capabilities. Responsibilities include setting the AI roadmap, enabling internal engineering and product teams, overseeing deployment and operational performance, managing quality and cost, and establishing evaluation and monitoring practices. The role also involves hiring, coaching, performance management, and developing a high-performing data science and machine learning organization.

Experienced Data Scientist supporting federal data-driven initiatives through statistical analysis, predictive modeling, machine learning, data preparation, visualization, dashboards, and actionable insights. The role collaborates with technical, business, and program stakeholders, advises on analytical methodologies, improves data quality, and contributes to modernization, automation, and emerging technology efforts.