Top Machine Learning Engineer Jobs in Chicago, IL

Design, develop, evaluate, and refine machine learning models using Python. Manage training data with MongoDB, build preprocessing and data pipelines, tune hyperparameters, benchmark model performance, document experiments, and deliver data-driven insights. Collaborate remotely with cross-functional contributors and support deployment or operationalization of models in cloud or enterprise environments.

Develop and refine machine learning models using Python, analyze datasets with MongoDB, and integrate preprocessing and data pipelines. Evaluate models, tune hyperparameters, benchmark performance, document experiments, and deliver data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize machine learning solutions in cloud or enterprise environments.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

Build and deploy advanced machine learning and agentic AI systems that improve Airbnb’s customer service experience. Responsibilities include developing AI assistants, integrating ML products, optimizing model performance, applying LLM techniques such as fine-tuning and RAG, building multi-agent architectures, and shipping scalable production ML systems. The role collaborates closely with Engineering, Product, and Design teams and contributes to emerging AI initiatives from concept through production.

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.

Develop, optimize, and deploy production-grade computer vision and deep learning models for real-time perception and navigation on edge hardware. Integrate AI systems with robots, drones, unmanned systems, and swarms while optimizing inference with CUDA and TensorRT. Manage containerized deployments using Docker and Kubernetes, contribute to CI/CD and automated testing, and transition research prototypes into reliable production features.

Set technical direction for production AI agents, LLM and vision-language model post-training, evaluation, MLOps, and data improvement systems. Architect agent orchestration, retrieval, observability, and failure recovery; establish quality and release standards; and ship measurable model improvements. This hands-on individual contributor will co-own the ML roadmap, lead technical reviews, mentor engineers, support hiring, and implement privacy-conscious, HIPAA-aware practices.

Lead evaluation and automatic optimization for AI models and agents, including benchmarks, LLM judges, simulations, trajectory analysis, regression tests, synthetic data pipelines, and post-training systems. Diagnose failures and convert findings into improvements to prompts, context, tools, rubrics, and agent harnesses. Apply current LLM research to production, collaborate with retrieval and product teams, mentor engineers, and guide Nova’s technical direction.

Design and build production machine learning systems for personalization, recommendations, and campaign optimization. Own the full model lifecycle, including experimentation, feature engineering, distributed training, deployment, latency monitoring, and online testing. Build scalable data pipelines and model-serving architectures with Databricks, Spark, AWS, Ray, and Kubernetes. Partner with platform teams, mentor engineers, and guide technical direction for machine learning capabilities.

Lead development of Iterable Nova’s retrieval and search systems for AI agents. Build hybrid retrieval across structured and unstructured data, semantic query understanding, entity and embedding pipelines, multi-hop search subagents, and evaluation frameworks. Improve relevance and grounding through ranking metrics, LLM judges, human labeling, regression testing, and online experiments. Mentor engineers and guide technical direction while taking systems from research through reliable production deployment.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Own the ML platform powering production training pipelines, model serving, deployment, orchestration, observability, and multi-region scaling. Drive major infrastructure initiatives, improve reliability and cost efficiency, lead incident response, collaborate across teams, and set the technical vision for ML operations. The role is hands-on, requiring production distributed-systems expertise, Kubernetes and cloud infrastructure knowledge, technical leadership, mentoring, and strong communication.

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.

Designs, deploys, and operates production machine learning systems for identity resolution, scoring, deliverability, and personalization. Responsibilities include feature engineering, model training, real-time inference, graph database integration, monitoring, drift detection, retraining, low-latency APIs, and scalable data processing. The role also contributes to MLOps standards, GPU acceleration, and ML architecture while partnering with platform engineering.

Lead the technical direction of Quora’s recommendation ecosystem, including feed retrieval, ranking, re-ranking, optimization, data pipelines, feature engineering, model training, and production integration. Drive measurable improvements through experimentation, apply generative AI, mentor engineers, and partner across product, data science, and infrastructure teams.

Lead the design, development, deployment, and operation of production-grade generative AI and LLM solutions. Build data and ML pipelines, fine-tune and evaluate models, implement RAG and prompt engineering, and establish MLOps practices on AWS. Drive use cases across insurance workflows, ensure security and responsible AI compliance, mentor engineers, and collaborate with product, engineering, security, finance, and go-to-market teams.

Own the end-to-end computer vision platform for fine-grained item identification and pricing. Responsibilities include designing and fine-tuning vision models, defining evaluation and confidence standards, building labeling feedback loops, prioritizing coverage versus accuracy, and deploying reliable, low-latency serving infrastructure at scale. The role also communicates model capabilities and trade-offs to technical and non-technical stakeholders.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Own the platform for deploying, serving, monitoring, and retraining fraud, risk, and payments models in production. Build real-time inference systems, reproducible feature and ML pipelines, model rollout workflows, drift monitoring, third-party model integrations, and merchant-facing risk tooling. Ensure low latency, reliability, explainability, training-serving consistency, and safe model operations for payment authorization systems.

Design, build, evaluate, and deploy LLM-powered agents and machine learning systems that improve Quora content quality and safety. The role involves developing agentic workflows, conducting offline and online evaluations, monitoring production performance, investigating failures, and improving accuracy, reliability, latency, and cost. New graduates will collaborate with experienced engineers and own projects from development through production.

Architect, deploy, operate, and monitor on-premises LLM infrastructure and agentic workflows. Build RAG pipelines, MCP servers, data pipelines, CI/CD integrations, testing environments, tracing and evaluation tools, and model-serving network requirements. Support Kubernetes-based AI/ML infrastructure and software development environments while collaborating with cross-functional teams. The role requires a bachelor's degree with four years of related experience or a master's degree with two years, plus TS/SCI clearance.

Leads architecture, deployment, monitoring, automation, governance, and lifecycle management for production machine learning systems. Manages datasets, ML pipelines, model registries, evaluation, drift monitoring, testing, rollback, security, compliance, and audit documentation. Partners with data, cloud, software, cybersecurity, testing, operations, and government stakeholders to deliver secure, traceable, mission-critical ML capabilities.

Build and operate production machine learning capabilities, including dataset management, automated training and deployment pipelines, model monitoring, drift detection, governance, testing, rollback, and secure cloud integrations. Collaborate with data, software, cloud, cybersecurity, testing, operations, and government teams. Maintain traceability, documentation, auditability, incident response, and human-review controls across the model lifecycle.