Top Machine Learning Engineer Jobs in Chicago, IL

Leads AI/ML engineering teams across platform and delivery pods, managing employees and contractors while enforcing code, agent, review, and production-readiness standards. Partners on reusable templates, pipelines, and evaluation processes, ensures adoption across teams, manages contractor performance and conversion, and removes delivery blockers. The role requires hands-on credibility reviewing agent architectures, prompts, and evaluation suites, with experience operating as a player-coach in fast-moving organizations.

Lead development of cutting-edge LLM and Agentic AI systems for Airbnb customer support, from research and prototyping to production deployment. Collaborate with product, design, and engineering to build scalable ML infrastructure, improve model performance, and implement evaluation, fine-tuning, and guardrails for chat and voice assistants.

Build and operationalize machine learning models for large-scale advertising optimization. Responsibilities include designing high-volume services, developing data solutions, training and evaluating models, improving calibration and guardrails, creating offline and online evaluation workflows, and monitoring production systems. The role requires collaboration with product and engineering teams, ownership of reliability and performance, participation in on-call rotations, and deploying scalable, cost-effective ML solutions.

Own the production lifecycle for machine learning and LLM-backed systems, including training and post-training pipelines, inference serving, model releases, monitoring, retrieval infrastructure, and cost and latency optimization. Partner with AI researchers and engineering teams to move prototypes into reliable production capabilities across customer tenants. Develop ETL and GraphQL features, enforce tenant isolation, and support deployments in regulated or customer-controlled environments.

Lead the architecture and development of Root’s machine learning platform for insurance pricing. Build infrastructure for feature pipelines and stores, reproducible training and orchestration, model registries, serving, validation, observability, and research-to-production workflows. Establish versioned contracts and technical standards, automate workflows using LLMs, write and review critical code, influence cross-team strategy, and mentor senior engineers. Collaborate closely with data scientists and researchers to create scalable, reliable, and reproducible ML capabilities.

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.

Lead enterprise AI platform automation across infrastructure provisioning, CI/CD, governance, observability, deployment, and lifecycle management. Build self-service capabilities for GenAI, data science, data engineering, and analytics workloads using Terraform, Kubernetes, cloud technologies, Python, and DevSecOps practices. Support agentic AI, event-driven architectures, distributed computing, and operational reliability while collaborating with engineering, architecture, governance, security, and business stakeholders. Provide technical leadership, design reviews, mentoring, and best-practice guidance.

Build and operate scalable ML infrastructure and platform capabilities across cloud and on-premises environments. Develop Python-based developer tooling, services, and production systems supporting experimentation, distributed training, model deployment, and operations. Lead complex initiatives from architecture through rollout, improve reliability and developer productivity, and partner with ML and infrastructure teams. The role emphasizes Kubernetes, AWS, distributed compute, GPU-intensive workloads, observability, and production excellence.

Designs, builds, and operates scalable machine learning platforms, pipelines, and infrastructure for model training, deployment, serving, and monitoring. Develops MLOps tooling, CI/CD workflows, model registries, feature stores, and experiment tracking. Ensures production reliability, observability, security, compliance, and cost efficiency across ML systems. Collaborates with product and engineering teams, integrates platforms with existing systems and data sources, and mentors engineers on reusable ML platform practices.

Build and deploy computer vision and machine learning solutions for power grid infrastructure, including defect detection, thermal anomalies, vegetation monitoring, and intrusion surveillance. Responsibilities span research implementation, model experimentation, error analysis, data pipelines, experiment tracking, model serving, testing, monitoring, and client delivery. The role requires productionizing models, adapting research papers, optimizing architectures, and independently owning projects from problem definition through deployment.

Own end-to-end computer vision and machine learning projects for power grid infrastructure, from problem framing and research experimentation through production deployment and monitoring. Develop detection, segmentation, classification, anomaly detection, and foundation-model solutions; build data pipelines, serving systems, experiment tracking, and model versioning. Conduct error analysis, benchmarking, tuning, code reviews, and testing while translating client requirements into reliable production models and communicating technical decisions and limitations.

Founding Engineer responsible for building FilmoreAI’s production AI and data platform for construction equipment dealers. The role covers agent infrastructure, model routing, LLM extraction and reasoning, canonical data modeling, entity resolution, Python data pipelines, Azure cloud orchestration, evaluation, observability, and safe human-approved write-back. The engineer will work independently in an ambiguous startup environment and use agentic development tools to ship reliable systems with strong cost, latency, accuracy, and operational guardrails.

Build and deploy low-latency machine learning models and inference infrastructure for Netflix’s advertising platform. Develop identity resolution, behavioral and contextual targeting, lookalike expansion, user signals, and audience features at massive scale. Collaborate with science, product, engineering, operations, design, and research teams to productionize models while supporting privacy, governance, security, and advertiser brand safety.

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.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Build and deploy LLM-powered agents and workflows using templates, pipelines, and guardrails. Develop comprehensive evaluations, integrate AI solutions with business systems and data sources, and instrument applications for cost and quality telemetry. Collaborate with business users to iterate within defined scopes. The role requires strong Python and TypeScript skills, production experience with LLM applications, API development, prompt and context engineering, retrieval, and healthcare data handling.

Provide on-call MLOps support for machine learning teams by triaging pipeline issues, debugging workflows, identifying root causes, and maintaining reliable model development operations. Collaborate with senior engineers to improve tooling, documentation, and processes, while communicating technical findings across teams. The role requires Python, foundational PyTorch and model pipeline experience, plus familiarity with CI/CD, monitoring, logging, and orchestration tools.

Design, build, deploy, and maintain production ML systems at scale. Collaborate with product and data science teams to create ML architectures, data pipelines, model training and monitoring, CI/CD for models, and ensure performance, reliability, governance, and Responsible/Explainable AI practices.

Develop and deploy machine learning models across the full lifecycle, including experimentation, MLOps, monitoring, explainability, and governance. Build scalable model-serving and feature pipelines, contribute to Spark-based data engineering in Databricks and Snowflake, and support orchestration, metadata, security, and compliance practices. Collaborate with platform teams on environment configuration and troubleshooting within HIPAA-governed and FedRAMP-compliant healthcare analytics environments.

Operate and improve enterprise AI/ML platforms across Palantir Foundry, AWS Bedrock, and Amazon SageMaker. Responsibilities include deployment, monitoring, observability, automation, governance, model lifecycle management, incident response, root cause analysis, CI/CD, infrastructure as code, and production reliability. The role provides technical leadership and mentorship while partnering with data, infrastructure, security, architecture, product, and engineering teams to support cloud-based AI workloads and Generative AI adoption.

Design, develop, and deploy enterprise AI applications using LLMs, RAG, AI agents, vector search, document processing, and cloud-native technologies. Build scalable APIs, integrations, knowledge platforms, and Kubernetes-based services while contributing to architecture, CI/CD, security, testing, and observability. Collaborate with business and technical stakeholders to solve complex problems, evaluate emerging AI technologies, and mentor team members.

Lead design, development, and production deployment of ML and algorithmic solutions. Own end-to-end ML systems, support distributed production reliability, communicate tradeoffs, and apply generative AI, LLM fine-tuning, RAG, and agentic techniques to product problems.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

Responsible for fine-tuning state-of-the-art LLMs, optimizing models for deployment, and driving AI architectural decisions. Collaborate with cross-functional teams to develop impactful AI products.

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