Top Machine Learning Jobs in Milwaukee, WI

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

Develop and experimentally validate novel ML architectures to improve prediction and design of in‑vivo molecular interactions, extending structure‑informed generative models to causal models and integrating with deployed drug‑design workflows.

Lead technical solution development for military and Federal civilian health capture and proposal efforts, ensuring technical compliance and producing quality architecture designs.

The Customer Success Engineer will lead deployments, serve as a technical advisor, and support customer engagement, focusing on product enhancements and user experience.

Support AI training initiatives by developing and refining financial models, translating market research and due diligence into structured AI training datasets, and partnering with stakeholders to convert complex financial concepts into training inputs. Evaluate and improve AI-generated financial analyses, identify gaps in modeling or valuation assumptions, and contribute strategic insights to enhance AI-powered private equity tools in a fully remote environment.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Create and curate high-quality graphic design datasets and training materials for AI systems. Collaborate with engineers to evaluate and refine AI-generated visuals, document design processes and style guides, and translate design concepts into actionable training data. Stay current with design trends and communicate feedback to cross-functional teams.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

Remote neurologist AI trainer who annotates and reviews clinical data, guides ML model training, interprets neurological diagnostics (EEG, EMG, LP), and communicates clinical insights to interdisciplinary teams to improve AI-driven diagnostic and treatment tools.

The Hyperscalers and Commercial Lead will develop technology-driven business strategies, drive customer adoption, and manage end-to-end sales processes while working closely with cross-functional teams.

The role requires a skilled Machine Learning Engineer to create complex models and AI solutions while collaborating with teams to enhance products and services.

The role focuses on leading editorial efforts to define industry narratives around energy infrastructure, translating regulatory changes into strategic content for executives, and influencing sales and positioning through high-impact communications.

The Principal Architect will lead AI/ML solutions, mentor teams, and collaborate with clients to design and deliver scalable systems using Google Cloud technologies, ensuring compliance with ethical standards.

Join Nextdoor's machine learning team to build data-intensive products, develop ML models, analyze datasets, and enhance user experiences. Collaborate closely with product and data science teams.

The Head of AI will define and execute an AI roadmap, lead research initiatives, mentor the AI team, and collaborate with senior leadership to enhance health technology.

The Director of Statistics leads statistical initiatives in phase 3b/4 and RWE studies, provides expertise in HTA and regulatory compliance, and collaborates with cross-functional teams. Responsibilities include statistical analysis plans, Bayesian methodologies, and mentoring junior staff.

Develop, deploy, and operationalize analytical and ML/AI models for cybersecurity use cases. Collaborate with product and engineering teams to enable and migrate production workflows, work with databases and data architectures, create visualizations and reports, adopt new cloud orchestration and data virtualization technologies, and mentor junior cyber data scientists.

Manage a portfolio of customers as primary contact, drive adoption and retention, respond to support requests, troubleshoot issues with product teams, identify upsell and renewal opportunities, and provide customer insights to product and engineering.

Manage a portfolio of SaaS customers, build relationships, monitor usage, prevent churn, handle support and troubleshooting, identify upsell/renewal opportunities, and relay customer feedback to product and engineering.

As a Senior Machine Learning Engineer, you will design and implement systems for ML and NLP, optimizing models and tools for Clover's healthcare solutions.

Lead the implementation of AI-driven supply chain solutions, collaborating with clients to enhance their business processes and ensure seamless technology integration.

Lead the design, development, and deployment of scalable machine learning and AI systems for enterprise clients, guiding architecture and mentoring engineers.