Top Senior Machine Learning Jobs in Chicago, IL

Lead Adyen’s Personalize Intelligence team developing machine-learning systems for real-time Checkout optimization and AI products. Provide technical leadership on architecture, reliability, and scalable services; coach machine learning scientists and data engineers; define goals and product roadmaps with Product; collaborate across Design, Engineering, Data, and leadership; monitor system performance; foster an inclusive, high-performing culture; and recruit new team members.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Lead the AI & Analytics stream to design, build, and deploy predictive and prescriptive ML models, integrate analytics into enterprise platforms (Salesforce, ServiceNow), automate root-cause and log analysis, translate stakeholder needs into production solutions, and manage analytics projects end-to-end while communicating results to technical and executive partners.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

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.

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.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.

Build and deploy production machine learning systems that optimize Samsara’s global hardware supply chain. Responsibilities include demand, inventory, lead-time, spend, and supplier-risk forecasting; anomaly detection; feature engineering and ETL; real-time inference; MLOps; data infrastructure improvements; governance; and model monitoring. The role defines the AI transformation roadmap, partners with executive and cross-functional stakeholders, mentors scientists, and drives adoption of AI-enabled operational workflows.

Design, build, evaluate, and productionize machine learning and LLM-based agentic systems for healthcare products. Own projects end-to-end, including problem framing, dataset development, modeling, evaluation, deployment, monitoring, and safety guardrails. Build ML infrastructure, data pipelines, inference systems, and evaluation frameworks. Collaborate with clinicians, product managers, and engineers, while setting technical direction and mentoring team members.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Develop and own end-to-end machine learning models for Uber Freight’s marketplace, including cost prediction, booking probability, demand elasticity, pricing, matching, recommendations, and optimization. Partner with backend engineers to deploy models to production and ensure reliable performance. Apply predictive modeling, causal inference, constrained optimization, reinforcement learning, and marketplace design to solve complex logistics problems.

Lead the development and production deployment of applied machine learning and LLM solutions across product, marketing, and operations. Own ranking, recommendation, filtering, personalization, experimentation, and model evaluation systems. Partner with business and technical teams to identify valuable use cases, establish modeling best practices, and ensure reliable production deployment. Manage and mentor applied ML practitioners while maintaining technical rigor, business alignment, and measurable customer and revenue impact.

Lead the design, development, deployment, and operation of production AI and ML systems. Build high-throughput Python services and Databricks pipelines, support traditional ML and LLM applications, establish testing and deployment standards, and develop monitoring for quality, reliability, latency, throughput, and cost. Partner with product, research, and responsible AI teams on governance and release criteria. Provide architectural leadership, conduct code reviews, mentor engineers, and resolve complex technical dependencies.

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.

Lead forensic technology engagements by designing and deploying machine learning models, statistical algorithms, and data pipelines for digital investigations and electronic discovery. Collaborate with clients to define goals, analyze cloud and enterprise data, develop reusable automation components, communicate findings to executive audiences, and deliver training. The role also involves managing people on high-profile consulting engagements and providing client-facing technical leadership.

Build and improve machine learning systems for ad ranking, relevance, personalization, and optimization. Responsibilities include feature pipelines, model training and evaluation, active learning, LLM-assisted labeling and synthetic data generation, experimentation, and low-latency production inference. The role partners with product, data, and platform teams to improve advertiser performance and user engagement while maintaining reliability, latency, and data quality.

Leads Tala’s Machine Learning Engineering team in building and operating platforms for model development, real-time inference, streaming features, batch processing, deployment, monitoring, and production reliability. Manages and develops 4–6 engineers, owns delivery and prioritization, provides architectural leadership, improves engineering practices and SLOs, and partners with Data Science, Data Engineering, Product, Credit, and Business Development teams.

Build and operate production machine learning systems and MLOps infrastructure for financial services. Responsibilities include developing feature and training pipelines, model-serving systems, CI/CD, automated retraining, monitoring, alerting, observability, and progressive model rollouts in GCP and Kubernetes. The role partners with data scientists and business teams to improve reliability, performance, scalability, cost efficiency, and deployment velocity while applying AI coding agents to infrastructure and ML workflows.

Leads and develops a perception and machine learning engineering team building computer vision, sensor fusion, tracking, detection, and scene-understanding capabilities for autonomous systems. Owns roadmap planning, technical decisions, delivery, model development and deployment workflows, field readiness, hiring, performance management, and cross-functional execution with autonomy, embedded, data, hardware, product, programs, and field operations teams.

Design, build, and maintain optimization technology, full-stack systems, and data-intensive scheduling infrastructure. Improve optimization algorithms using linear optimization, machine learning, and generative AI. Lead complex technical initiatives, establish testing and observability, support scalable operations, troubleshoot customer issues, participate in on-call rotations, and mentor engineers while partnering closely with product and engineering teams.

Evaluates emerging machine learning technologies and designs scalable, secure enterprise architectures covering data ingestion, feature engineering, training, deployment, monitoring, governance, and responsible AI. Partners with engineering, data science, platform, risk, and security teams to establish MLOps standards, reusable solution patterns, and production deployment strategies. Provides technical leadership, evaluates vendors, guides implementation, and translates complex ML architecture recommendations for technical and non-technical stakeholders.

Responsible for validating payment network models, including fraud and AML. Engage with stakeholders, improve model accuracy, and leverage innovative technology for data analysis.

Build and scale production AI systems for Prophetic’s real estate intelligence platform. Responsibilities include developing agentic systems, data pipelines, inference workflows, evaluation benchmarks, geospatial processing, and machine learning applications across hundreds of millions of records. The role requires shipping reliable Python services, optimizing throughput and cost, integrating research into products, and collaborating with engineering and product teams.