Top Machine Learning Engineer Jobs in Chicago, IL

Build and operate ML training and serving infrastructure by designing, developing, and launching backend systems. Collaborate across teams to decompose projects, support operations and on-call, create monitoring and metrics, perform code reviews, and contribute to developer velocity and platform reliability.

As a Principal Machine Learning Engineer, you will develop multimodal AI experiences, design agent interactions, and lead cross-team initiatives while ensuring AI safety and reliability.

Design and deploy machine learning models for personalized recommendations and search on Zillow's platforms. Collaborate with cross-functional teams to enhance user experience.

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.

The Staff Machine Learning Engineer will enhance Quora's recommendation systems, lead machine learning projects, and own end-to-end ML processes.

The AI/ML Engineer develops AI features for Cengage's educational products, focusing on enhancing student engagement and learning outcomes through shipping production AI capabilities.

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.

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

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.

The Machine Learning Engineer will design, build, and maintain scalable ML systems, optimize ML pipelines, implement MLOps, and collaborate with teams to deploy models into real-time products.

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.

As a Staff Machine Learning Engineer at Twilio, you will build and maintain data pipelines and ML infrastructure, ensuring high-quality data and model performance while collaborating with product and security teams.

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.

The role involves developing, adapting, and implementing machine learning algorithms for computer vision tasks in power grid analysis. Responsibilities include experimentation, error analysis, and project ownership.

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

Develop and productionize NLP and ML solutions (including LLMs and GenAI) to extract insights from unstructured data. Manage research, data preparation, model training/fine-tuning, evaluation, and cross-functional integration into production systems, focusing on sparse data, model explainability, and performance.

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.

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

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

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.

The Principal Machine Learning Engineer will define technical direction, architect scalable systems, lead cross-organizational initiatives, mentor engineers, and communicate effectively at all levels.

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

Design, develop, and deploy machine learning models; analyze large datasets; build scalable data pipelines and distributed computing environments; integrate ML solutions with cross-functional teams; optimize performance and support production ML infrastructure.

The Machine Learning Engineer will design and build systems that improve the AI shopping agent by using real user feedback, focusing on feedback loops and metrics to enhance user interactions and agent performance.

Deploy, optimize, and scale ML models for datacenter and edge AV devices; own model compilation and deployment pipelines; profile and enforce latency/throughput budgets, and apply quantization, distillation, and pruning to meet onboard compute constraints.