Top Machine Learning Engineer Jobs in Chicago, IL

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 design, development, and production deployment of AI/ML solutions and conversational chatbot platforms. Own end-to-end implementation, MLOps/CI-CD, containerized deployments, solution architecture, security and governance, mentor engineers, and collaborate with product, enterprise architecture, and vendors to deliver scalable, monitored AI systems.

The role involves designing and implementing AI and machine learning solutions, transforming data into insights, and supporting client engagements. Responsibilities include model development, data analysis, and enhancing model performance using established technologies.

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.

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

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 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.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

Lead technical direction and delivery of ML platform infrastructure (feature store, model serving, experimentation, training pipelines). Drive architecture, resolve system-level issues, write production code, mentor engineers, and partner with ML, data, and product teams to enable scalable, reliable ML model development and deployment.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

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

Design, build, deploy, and operate production-grade AI/ML solutions and LLM-powered, agentic applications across Azure, AWS, and hybrid environments. Implement end-to-end pipelines (ingestion, prompt engineering, deployment, monitoring), integrate with enterprise data platforms, ensure security/privacy compliance, and collaborate with engineering and business teams to support lifecycle management and production support.

Design, train, and implement domain-specific LLMs for automation in restoration companies. Collaborate with teams, coordinate AI engineering, and ensure model reliability.

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.

Lead the technical direction of a computer vision platform for sidewalk inspection. Responsibilities include managing ML models, improving data pipelines, and architecting systems for terabytes of geospatial data.

Lead the technical direction of DeepWalk's computer vision platform, enhancing ML systems and data pipelines for sidewalk inspection.

Lead a Labs team as a hands-on engineer building hybrid AI/optimization systems: design ML-augmented optimization, prototype and productionize solutions, architect MLOps/CI-CD, benchmark solvers, mentor engineers, and collaborate with researchers and stakeholders to scale reusable components and APIs across Kroger.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

Design, build, and maintain scalable self-service data and ML platform tools and workflows. Mentor junior engineers, collaborate with cross-functional teams, implement monitoring/alerts, drive infrastructure improvements, integrate new technologies, and produce technical documentation to support reliable, scalable data products and pipelines.

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.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring 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.

As a Staff Software Engineer, you will lead the development of computer vision systems for sidewalk inspection, overseeing model lifecycle, system architecture, and mentoring while improving deployment practices.