Top Senior Machine Learning Engineer Jobs in Chicago, IL

Develop and deploy machine learning models and production software for multi-object tracking in autonomous trucks. Responsibilities include sensor fusion across camera, LiDAR, and radar; scalable PyTorch training workflows; tracking algorithm development; large-scale data analysis; C++ and Python engineering; safety and validation strategy; architecture decisions; and cross-functional collaboration. The role also provides technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

Develop and optimize large-scale advertising ranking models for CTR and CVR prediction, calibration, user representations, and sequence modeling. Own machine learning systems end to end, including data pipelines, feature engineering, training, evaluation, deployment, and maintenance. Collaborate with platform, product, data science, and engineering teams to run experiments and improve advertiser outcomes, revenue, and user relevance. Apply deep learning and recommendation techniques within production latency, reliability, and cost constraints.

Design, train, deploy, monitor, and optimize speech and NLP models for a voice-agent platform. Build MLOps pipelines, APIs, and inference services while developing Android and iOS applications that integrate AI, voice, real-time communication, and backend services. Create frontend prototypes and dashboards, conduct model experiments, evaluate vendors, optimize performance, document systems, and maintain engineering quality across collaborative teams.

Build and deploy production machine-learning systems within client environments, covering data discovery, modeling, validation, deployment, scheduling, monitoring, and retraining. Develop forecasting and detection models for imperfect time-series data, establish honest evaluation and KPI frameworks, and ship usable APIs or interfaces. Work directly with client IT and data teams through discovery, demos, and feedback cycles. The role also involves data engineering, software development, basic cloud and DevOps work, and advising clients when ML is not appropriate.

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.

Develop and maintain open-source Python frameworks and ML developer tools, architect scalable libraries, grow early-stage projects, build real-time streaming capabilities, integrate modern frontend technologies, and collaborate with open-source contributors and the broader developer community.

Build and improve machine learning systems for ad ranking, relevance, and optimization. Responsibilities include developing active learning and LLM-assisted labeling workflows, owning experimentation and evaluation, and deploying low-latency inference services. The role partners with product, data, and platform teams to improve advertiser performance and user engagement while ensuring production reliability, scalability, latency, and data quality. Candidates will use Python, Go, AWS, distributed systems, and AI-assisted development tools.

Own the full lifecycle of clinical text extraction models, from problem framing and annotation strategy through production deployment, evaluation, monitoring, and retraining. Build clinical NLP pipelines, establish defensible quality and error-analysis systems, and apply LLMs using rigorous evaluation, observability, and cost controls. Partner with clinical data and backend engineering teams while addressing PHI protection, de-identification, auditability, and access controls. As the first ML hire, set technical direction and potentially build and lead the future ML team.

Builds and maintains full-stack software products and backend services with a strong Java and Spring Boot focus. Develops APIs, UI, infrastructure, CI/CD pipelines, and production machine learning or GenAI systems. Practices test-driven development, supports incident response, addresses security and compliance findings, performs root-cause analysis, and coaches junior engineers. Collaborates on architecture, participates in Agile ceremonies, and improves observability, deployment readiness, and product quality.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

Design, build, and deploy high-throughput ML and LLM-powered personalization and agentic systems at consumer scale. Process petabyte-scale data, optimize real-time inference, implement resilient fallback paths, and balance latency, cost, and model quality while collaborating across engineering and product teams.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.

The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Design, build, and maintain scalable self-service data and ML platform tools and workflows. Mentor junior engineers, collaborate with cross-functional teams, implement monitoring/alerts, drive infrastructure improvements, integrate new technologies, and produce technical documentation to support reliable, scalable data products and pipelines.

Leads the architecture, development, deployment, and optimization of enterprise machine learning solutions. Responsibilities include designing scalable ML systems, developing predictive models, supporting MLOps and model lifecycle management, improving data quality and feature engineering, implementing governance and validation frameworks, and mentoring technical teams. The role also contributes to AI strategy, innovation initiatives, responsible AI practices, and organizational modernization.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

Productionize and optimize ML systems for AI reasoning over structured data. Responsibilities include improving training and inference performance, porting workloads from CPU to GPU, building training and deployment infrastructure, developing experimentation and observability tools, and implementing new model architectures and fine-tuning methods using Python and PyTorch. The role requires strong ML research, systems engineering, software architecture, optimization, and experimental design skills.

Develop and deploy advanced ML solutions for OCR and NLP, optimizing models for performance and integrating them into production workflows while collaborating with cross-functional teams.

Design, build, and operate core AI capabilities and reusable model integrations for an enterprise platform. Integrate foundation models into production, optimize performance and reliability, prototype new techniques, and support scalable backend services and distributed AI systems in collaboration with cross-functional teams.

Build and productionize large-scale ML models and pipelines for query understanding and personalization. Work on autocomplete, query tagging/NER, query expansion, intent modeling, and LLM-powered natural-language search. Collaborate cross-functionally to design, deploy, and monitor batch and real-time systems, leveraging modern ML infrastructure and tooling to improve search relevance across Airbnb products.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.