Top AI Research Jobs in Chicago, IL

Research, design, train, and deploy multimodal AI models using clinical and molecular data to support precision medicine, biomarker discovery, therapy outcome prediction, and companion diagnostics. The role requires collaboration with life sciences companies, regulatory alignment for healthcare products, and publication of original research. Responsibilities include working with pathology slides, genomic profiles, spatial omics, and longitudinal patient records while contributing hands-on model development and product-level engineering.

Conduct research on low-latency machine learning inference and hardware acceleration. Architect and implement an ML research project, collaborate with hardware engineers, evaluate neural architecture search and quantization methods, and deploy solutions on FPGAs or custom ASICs. Present findings and deliver a prototype or benchmark while assessing performance, engineering costs, and industry impact.

Own product backlog and roadmap for knowledge and semantic platform capabilities (ontology, knowledge graph, taxonomy, metadata). Conduct discovery with internal clients, define semantic models, entity relationships, and requirements; partner with engineering, data, and AI teams to deliver reusable platform services; promote governance, data stewardship, validation, adoption, and metrics to improve semantic quality and AI-enabled product delivery.

Conduct research and prototype learning-based methods for perception, planning, and control in embodied AI and robotics. Collaborate on navigation, manipulation, orchestration, and real-world robot systems. Evaluate vision and machine learning models for robotics applications and contribute to peer-reviewed publications, patents, prototypes, and other technical outcomes.

Leads Year Up United’s AI innovation portfolio, maintaining visibility into initiatives, managing intake and governance, running low-cost pilots, and making evidence-based go/kill decisions. The role evaluates emerging AI capabilities, builds organizational AI fluency through communities and Champions, partners cross-functionally, and hands validated pilots to technical teams for scaling. It also includes mentoring students and occasional travel to organizational sites.

Research and develop AI/ML models and advanced DSP algorithms for intelligent audio products. Responsibilities include defining requirements, training and optimizing models, building low-latency prototypes, collecting and annotating data, evaluating performance, documenting research, and collaborating with software, product, signal processing, and test engineering teams to transition concepts into products.

Develops, evaluates, and deploys AI foundation and generative models for weather, hydrology, and earth system applications. The researcher will train and optimize deep learning models on large GPU-based HPC systems, analyze atmospheric and hydrological datasets, benchmark model performance, and collaborate with scientists and public and private sector stakeholders. Responsibilities also include scientific coding, technical analysis, publications, reports, and documentation.

Support consulting engagements and research across the semiconductor and AI stack by performing technical analysis, cross-stack research, analytical modeling, and preparing client-facing reports, presentations, and briefings. Track industry trends, evaluate hardware and AI model developments, and collaborate with senior consultants to translate technical insights into strategic recommendations.

As a Curriculum Specialist, you will maintain and enhance computer science curriculum, create instructional materials, and support teachers while ensuring quality and alignment with standards.

Leads AI and machine learning research and engineering teams developing autonomous systems and mission applications. Oversees algorithm research, model development, prototyping, testing, deployment, and optimization across reinforcement learning, deep learning, computer vision, signal processing, and control systems. Manages workloads, resources, project delivery, technical standards, stakeholder communication, and employee development while identifying opportunities to apply emerging AI technologies.