Top Machine Learning Engineer Jobs in Chicago, IL

Architect and co-design machine learning models for custom hardware, accounting for latency, resource, and numerical constraints. Translate model requirements into hardware architecture decisions, collaborate with hardware engineers to implement and verify inference solutions, and deploy systems from proof of concept through production. Evaluate research in neural architecture search, ML systems, quantization, and compiler infrastructure for measurable performance improvements.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Lead the architecture and development of large-scale machine learning systems, distributed training infrastructure, ranking systems, and production ML services. Drive complex initiatives from concept through deployment, establish engineering standards, partner with product and engineering leadership, and mentor data scientists and engineers. Build reliable, low-latency, high-availability AI platforms using advanced ML frameworks, accelerators, and big data technologies.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Build, test, deploy, and evaluate machine learning models using established frameworks. Support feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborate with engineering and product teams to define AI requirements, research performance improvements, and respond to critical IT incidents. The role requires advanced quantitative education, 5+ years of AI/ML engineering experience, Python expertise, and knowledge of deep learning and generative AI.

Build and scale production machine learning solutions for lottery fulfillment. Develop and optimize computer vision and multimodal models, fine-tune and evaluate models, create data pipelines and evaluation frameworks, and deploy models on cloud infrastructure. Integrate models into real-time systems involving image processing, barcode decoding, and OCR. Collaborate cross-functionally, improve engineering standards, mentor teammates, and support reliable nationwide expansion.

Leads the design, development, deployment, and automation of production-grade machine learning and optimization solutions. Builds scalable data and ETL pipelines, internal ML and ERP tools, and reusable engineering frameworks. Ensures reliability through software engineering and DevOps practices, evaluates emerging technologies, documents architectures, collaborates cross-functionally, and mentors team members. Work includes AWS, Databricks, optimization modeling, web applications, databases, orchestration, containers, and machine learning frameworks.

Design, build, deploy, and support production AI applications for complex enterprise workflows. Develop LLM, retrieval, agentic, API, and distributed-service capabilities; create evaluation, testing, tracing, and observability practices; investigate AI failures; and optimize quality, security, performance, maintainability, and cost. Collaborate with domain experts, architects, data teams, and software engineers to translate specialized knowledge into reliable, explainable, and maintainable solutions.

Develop and validate AI/ML proofs of concept, including agentic systems, MCP integrations, LLM workflows, and cloud-based analytics solutions. Build production-grade Python applications, APIs, microservices, and MLOps pipelines; integrate agents with Azure, AWS, GCP, or large-scale data platforms. Responsibilities include data exploration, workflow automation, testing, monitoring, governance, technical documentation, solution design, mentoring, and communicating AI architectures to stakeholders.

Own the ML platform layer connecting data science and infrastructure. Responsibilities include managing the feature store, building self-service training and deployment platforms, standardizing retraining, scaling batch and online inference, implementing monitoring, and establishing standards for model and feature versioning, lineage, testing, and documentation. The role partners closely with data scientists and backend engineers to improve model delivery speed, reliability, and independence across multiple teams.

Build and evolve machine learning tooling and infrastructure for AI-powered clinical solutions, including automated prompt tuning, feedback loops, model benchmarking, experimentation, and evaluation. Partner with data science, clinical, software engineering, and product teams to develop scalable solutions, solve ambiguous technical problems, establish engineering standards, and provide technical mentorship without direct management responsibility.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Owns the infrastructure and production lifecycle for product and marketing machine learning models. Responsibilities include real-time model deployment, feature pipelines and stores, monitoring and alerting, CI/CD, retraining infrastructure, marketing model operationalization, data pipelines, ad-platform feedback loops, and dependency management. The role bridges data science and engineering while mentoring teammates and making pragmatic architecture decisions.

Researches, designs, develops, trains, and deploys computer vision and machine learning models. Builds and maintains ML software, testing and validation processes, and documentation. Applies image and video processing, deep learning, and computer vision techniques across edge and cloud-based platforms while improving existing applications and performance.

Build, deploy, scale, and operate production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, managing cloud-based distributed systems, automating testing and deployment, monitoring production models, and applying responsible AI practices. The role collaborates with Product and Data Science teams in an Agile environment and uses technologies such as Python, PyTorch, Spark, Kubernetes, and cloud platforms.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring models, applying responsible AI practices, and collaborating with product, data science, and engineering teams. The role requires advanced programming, machine learning, distributed computing, Kubernetes, and cloud experience.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Build, deploy, scale, monitor, and maintain production machine learning models and infrastructure. The role involves developing ML components, optimizing algorithms and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, and applying responsible AI and model governance practices. Candidates collaborate with product, data science, and Agile engineering teams while using technologies such as Python, PyTorch, Spark, Kubernetes, and major cloud platforms.

Designs and delivers end-to-end AI/ML solutions on Google Cloud, including data ingestion, feature engineering, model training, deployment, and monitoring. The role leads client discovery, translates business needs into AI/ML strategies, develops roadmaps, provides technical leadership, and supports testing and user training. It requires hands-on Gemini and GCP expertise, customer-facing consulting experience, and collaboration across engineering and product teams.

Lead development and production deployment of machine learning systems for time-series anomaly detection in connected-device environments. Build streaming and MLOps pipelines, tune alerts, monitor model drift, and integrate AI systems with LLM-based assistants. Guide architecture, tooling, observability, and production readiness while mentoring and managing applied AI and full-stack engineers. Collaborate across engineering, product, data, and research teams.

Design and build production-grade AI memory systems, including retrieval pipelines, storage tiers, privacy filtering, memory extraction, adaptive updates, hybrid search, and evaluation frameworks. Ship and maintain LLM-powered features, diagnose RAG and prompt failures, implement agentic workflows and tool integrations, and ensure reliable degradation and fallback strategies. Collaborate with product management to define feasible memory-system capabilities while owning production reliability.

Build and scale pipelines that transform conversation data into structured customer memory. Develop LLM extraction and retrieval systems using embeddings, vector search, and ranking; create evaluation benchmarks and regression gates; diagnose production issues through logs, metrics, and traces; and partner with Product and ML teams to improve customer-focused AI systems.

Build and optimize scalable pre-training pipelines for foundation models. Implement distributed training across GPUs, TPUs, and high-performance clusters; translate research experiments into production workflows; develop monitoring and fault-tolerance systems; and benchmark and tune performance across hardware and software stacks.

Develop AI agents, chatbots, and machine learning models for a modernized federal data science platform. Research and standardize AI capabilities in Databricks and Palantir, prompt-engineer LLMs, integrate AI into DevOps pipelines, and train models on production datasets for prediction and anomaly detection. The role requires collaboration with developers and customers, database familiarity, Agile experience, documentation, and an active or obtainable DoD Secret clearance.