Top Machine Learning Jobs in Milwaukee, WI

Design and implement ML systems for ads targeting, drive technical direction, mentor junior engineers, and collaborate cross-functionally for product development.

The role involves designing and scaling ML infrastructure for LLMs, including building training pipelines and deploying models in cloud and on-prem environments. Collaboration with engineers and managing GPU/TPU workloads is key.

The role involves leading ML/AI solutions, managing technical roadmaps, collaborating with stakeholders, and driving innovation in products and services.

The Senior ML Engineer will design and deploy large-scale ML systems for personalized recommendations, directly impacting user engagement and revenue. Responsibilities include building models serving 100M+ predictions daily and optimizing data processing pipelines.

The role involves building and optimizing large-scale ML systems for personalized recommendations that drive engagement, retention, and revenue, impacting millions of users daily.

The role involves building and optimizing machine learning systems for ad performance, collaborating with teams to enhance targeting and revenue. Responsibilities include developing algorithms, conducting experiments, and improving infrastructure for scalable ad decisioning.

As a Senior ML Engineer, you will develop and optimize ad algorithms to enhance engagement and revenue, collaborate with teams, and implement scalable ML systems.

The Machine Learning Engineer will build and scale ML models, collaborate with cross-functional teams, and improve real-time recommendations for Stitch Fix's clients.

Design and architect AI/ML solutions, build document parsers and classifiers, maintain AI systems, and collaborate with cross-functional teams.

The Software Engineer will develop machine learning infrastructure, optimize models, and create tools for data analysis while collaborating with a diverse team.

The role involves sourcing and recruiting senior engineers in AI, ML, and related fields, building talent pipelines, and collaborating with engineering leadership.

The role involves developing scalable ML solutions, leading cross-functional discussions, analyzing knowledge data, and producing technical documentation to improve search relevance using AI and machine learning technologies.

Lead the design and implementation of deep learning models for biological data, focusing on model architecture, project leadership, and innovation in machine learning applications in biology.

Design and implement software solutions for machine learning operations, focusing on geospatial imagery analytics and MLOps software development.

The Machine Learning Engineer III will design, develop, and deploy machine learning models and algorithms, mentor junior staff, and collaborate with cross-functional teams to solve complex business problems.

As a Machine Learning Engineer, you'll optimize and deploy production ML models, build inference services, and collaborate closely with teams to integrate new models and enhance performance.

The ML/AI Engineer will define machine learning direction, develop automated pipelines, integrate models, and collaborate with product teams to solve user problems.

Design and build recommendation and personalization systems, conduct A/B testing and causal inference, and mentor the data team in best practices.

The Applied ML Engineer at Ideogram will turn generative models into production features, collaborate with teams to define metrics, and ensure ML systems' reliability.

As a Principal ML Engineer, you will design ML architecture, tackle technical challenges, mentor engineers, and influence ML strategy across the company.

The Sr. Staff Machine Learning Engineer will lead architecture and development of ML systems, enhance patient interactions, optimize treatment recommendations, and mentor engineers.

The Senior Machine Learning Engineer will design and maintain ML solutions for fraud detection, collaborating across teams and establishing MLOps best practices.

The role involves developing cloud solutions and enhancing ML/AI pipelines, ensuring service availability, and automating deployments in a scalable environment.

Develop and support ML infrastructure for large-scale scientific workflows, improve automation processes, and contribute to product features in AI-enabled drug discovery.

Develop and optimize machine learning models for processing financial documents, mentor junior staff, and collaborate across teams to enhance software products.