Top Machine Learning Jobs in Milwaukee, WI

The Automation Engineer will assist in developing and implementing industrial automation systems, including PLC programming, SCADA design, and data integration.

Lead globally distributed teams in software engineering, focusing on DevSecOps, AI-driven practices, and continuous improvement while fostering high-performing teams.

The Principal Product Manager will lead the Underwriting team to improve credit risk models, manage vendor relationships, and analyze portfolio performance.

The Staff Research Scientist will conduct AI research, innovate in LLM and information retrieval, and collaborate with research teams.

As a PhD Systems & AI/ML Research Intern, you'll work on infrastructure problems, build prototypes, run benchmarks, and document findings.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Lead the development of audit practices, enhance methodologies, integrate data analytics, ensure compliance, mentor teams, and communicate effectively with leadership.

Manage global technical support teams for MongoDB, focusing on customer satisfaction through technical issue resolution, performance enhancement, and team development.

Lead the development of machine learning systems for Cedar's Personalization Engine, focusing on ML modeling, MLOps, and team management to drive healthcare solutions.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

As a Risk Manager, you'll analyze and mitigate security risks, implement fraud defenses, and collaborate with cross-functional teams to enhance security objectives.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

Lead a team of ML engineers to develop computer vision models, manage ML roadmaps, and improve product functionalities through innovative technologies.

Lead the development and execution of ML platform strategies, manage a high-performing team, and enhance 3D deep learning applications.

This role involves designing and building advanced ML models to protect against fraud and enhance user experience while mentoring lower-level engineers and influencing technical roadmaps.

The role involves designing, developing, and deploying machine learning models for healthcare applications, collaborating with cross-functional teams, and optimizing model performance.

Lead the evaluation and optimization of large-scale datasets for training AI models, ensuring data quality and collaboration with research teams.

Lead evaluation and optimization of large-scale datasets for AI models. Design statistical methods, collaborate on data strategies, and enhance data quality.

Lead the evaluation and optimization of large-scale datasets used for AI training, ensuring data quality through statistical and machine learning methods.

Lead evaluation and optimization of large-scale datasets for AI model training, ensuring data quality and collaborating across teams.

As a Software Engineer at Speechify, you'll enhance AI Voice products, optimize ML pipelines, and improve deployment tools in a fast-paced environment.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

Lead design and implementation of enterprise-grade AI systems, architect AI infrastructure, and drive technical strategy for AI-powered products primarily for military applications.

As a Senior Machine Learning Engineer, you will develop scalable ML solutions, collaborate with teams, optimize models, and implement best practices for deployments and infrastructures.

The Senior Applied Scientist II is responsible for designing and implementing ML models to optimize the supply chain, collaborating across teams, and driving data-driven decision-making.