Top Machine Learning Engineer Jobs in Chicago, IL

Design, build, and deploy AI systems across the full lifecycle: data pipelines, model development (traditional ML and generative AI), agent-based applications, API integration, observability, and evaluation. Collaborate with architects and domain experts, support experimentation and production deployments, and assist with project coordination and stakeholder communications.

Design and build AI assistants for interactive 3D workflows: build NLP pipelines, integrate cloud and client LLMs, design knowledge graphs, fine-tune foundation models, prototype AI features, and improve personalization and system performance.

The Lead AI/ML Engineer evaluates emerging technologies, leads teams in adopting solutions, and provides technical leadership while mentoring junior specialists.

As a Machine Learning Engineer, you'll design and implement detection systems, enhance model efficacy, and analyze datasets to improve email detection against security threats.

Lead development and implementation of AI-driven solutions focused on text classification and NLP. Build and optimize ML models, preprocess and transform text data, combine supervised/active/semi-supervised approaches, create tagged datasets, and provide technical leadership while collaborating cross-functionally to align AI capabilities with product and business goals.

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 deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Build and maintain data and model layers for an AI decision‑support system: data ingestion, probabilistic record matching, calibrated scoring, provenance‑tracked retrieval and grounded generation, and secure production pipelines. Ensure explainability, auditability, and accredited deployment in a controlled government cloud environment.

Design and build data and model layers for an accredited AI decision-support system: record matching, calibrated scoring, provenance-tracked retrieval and generation, secure ingestion pipelines, model packaging/serving, and instrumentation for audit and accreditation.

Lead engineering execution for predictive and applied AI model portfolios across pricing, underwriting, and sales. Build, deploy, and operate ML/AI pipelines and production services in AWS/GCP, support generative and agentic AI initiatives, ensure governance, monitoring, and production readiness, and mentor junior engineers.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

Build, train, fine-tune, evaluate, and deploy machine learning and LLM models for production. Create monitoring, evaluation, and feedback tooling, evaluate new techniques, and collaborate cross-functionally to ship ML-powered features.

The Staff Machine Learning Engineer will lead AI initiatives, architect LLM systems, design evaluation frameworks, and mentor engineers.

Design, build, and deploy machine learning systems for controlling quantum processors. Develop reinforcement learning and Bayesian methods for QEC calibration, and work cross-functionally with product and R&D teams.

Design, build, and integrate AI-enabled applications using C#/.NET and Azure. Develop data ingestion, transformation, and automation pipelines for structured and unstructured data. Implement and optimize LLM/Generative AI solutions, build APIs, and support end-to-end delivery from design through production handoff while partnering with business stakeholders.

Lead design, build, and operation of end-to-end production ML systems (feature pipelines, training, inference, experimentation, monitoring) for ranking, recommendation, rewards, ROAS/LTV prediction, personalization, and offer optimization in a high-scale AdTech environment. Provide hands-on technical leadership, mentor engineers, drive MLOps and observability standards, and partner with Data Engineering, BI, Product, and business teams to connect models to outcomes.

Develop and deploy end-to-end machine learning solutions, collaborate with cross-functional teams, and implement MLOps frameworks while monitoring model performance in production.

The Senior AI Machine Learning Engineer will design and implement MLOps services, build AI solutions, and lead collaborations across multiple teams to enhance customer experiences.

This role involves designing, deploying, and maintaining machine learning and AI models in production, managing ML pipelines, and ensuring system reliability and performance.

The Staff Engineer will design and manage ML infrastructure while mentoring teams, driving MLOps strategies, and ensuring compliance in healthcare data systems.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.

Design and develop machine learning systems for construction robots, including scalable infrastructure, computer vision models, and data analysis for performance improvement.

The Machine Learning Engineer will develop editable visuals combining computer vision and machine learning, enabling chat-driven figure editing and leveraging biological understanding to produce accurate visuals.

Design, build, and deploy scalable AI and machine learning solutions, leading full AI lifecycle, collaborating with teams, and ensuring systems align with client needs.