Top Machine Learning Jobs in Milwaukee, WI

You will execute and improve insurance workflows, analyze operational efficiency, collaborate with product teams, and support onboarding and documentation efforts.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Build and deploy production-grade LLM applications, AI agents, models, and scalable RAG pipelines for investment and business workflows. Architect secure MCP integrations with enterprise systems, establish MLOps and LLMOps practices, optimize model performance and latency, and develop evaluation, monitoring, governance, and rollback processes. Collaborate with data engineering and platform teams to provide secure, governed access to organizational data.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

As a Research/ML Engineer, you will translate cutting-edge AI research into product solutions, develop safety and reliability systems, and collaborate with technical teams.

Lead development of safety frameworks and V&V for end-to-end autonomous robotics, perform risk assessments and safety reviews, derive technical safety requirements, and coordinate cross-functional teams to monitor fleet performance and deploy scalable autonomy.

Lead design and implementation of scalable data models and analytical tooling for Consumer Product. Partner cross-functionally to translate business needs into reliable SQL/Python data products, dashboards, and automated pipelines using engineering best practices (CI/CD, testing, version control, monitoring). Drive data-informed strategy and manage long-term infrastructure while supporting ad hoc requests.

Evaluate and author high-quality computer science content to improve AI model reasoning and accuracy. Analyze complex CS problems, create scenarios/datasets, review AI outputs for correctness, and collaborate remotely to refine guidelines and documentation.

Co-found and lead a stealth startup building an agentic AI platform embedded in CAD tools to automate geometry updates, feature-tree edits, and cross-tool workflows. Drive product, engineering, and commercial strategy, hire the founding team, and develop a defensible data capture and automation stack for industrial CAD workflows.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

Develop and implement AI solutions, optimize models, preprocess data, integrate AI into workflows, and collaborate with cross-functional teams.

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.

The Research Advisor will provide high-level consultancy for generative AI systems, engage in research collaborations, and represent the Human Frontier Collective in key executive meetings.

The Director of AI will lead AI and machine learning initiatives, develop AI architecture for humanoid robots, oversee production-ready AI systems, and manage team growth and budget.

Lead a team to enhance Cengage Learning Platforms by implementing AI-first strategies, improving software delivery processes, and ensuring product quality.

Lead strategy and execution for AI-powered Customer Success Services, overseeing Gainsight and related platforms, customer community strategy, customer intelligence, automation, digital engagement, platform governance, and a team to scale adoption, retention, and operational efficiency across the customer lifecycle.

Develop and expand software products and platforms across backend, frontend, cloud, AI, and machine learning systems. Build efficient, reliable, and testable code; collaborate with business partners to launch features; influence product and engineering decisions; mentor team members; and help shape team culture and product vision in an agile environment.

Lead divisional FP&A including monthly/quarterly forecasting, annual planning, SG&A and SKU-level profitability analysis. Partner with commercial, supply chain, operations and product teams to drive cost reduction, forecast accuracy, inventory rightsizing, and strategic initiatives. Promote systems and process improvements, automation and analytics to support executive decision-making.

Develop ML/RL models and infrastructure for AV simulations, optimize training loops, and support project timelines in autonomous vehicle tech.

Design, build, and deploy AI-powered data automation workflows to replace manual data quality and enrichment processes. Integrate third-party enrichment platforms, implement monitoring and governance, define quality metrics, and enable self-service automation across CRM, marketing, and sales data to improve accuracy and efficiency.

Own product strategy, vision, roadmaps, backlogs, and delivery for enterprise SaaS products serving financial services organizations. Translate strategy into requirements and user stories, lead Agile ceremonies, engage customers, monitor product metrics, manage delivery risks, and align portfolio companies with leadership and cross-functional teams. Identify and deliver AI-enabled capabilities, workflow automation, analytics, and intelligent business solutions.

Full-cycle quota-carrying Account Executive selling Bold Metrics' SaaS fit and sizing solutions to apparel brands. Own outreach through contracting, build ROI cases, manage cross-functional coordination, develop outbound strategies, leverage AI to scale workflows, track funnel metrics, and feed customer insights to Product.

Lead and mentor full-stack engineering teams to design, build, test, deploy, and support scalable client-facing and proprietary software. Drive architecture, technical roadmap, Agile delivery, cloud-native migrations, API integrations, quality metrics, and cross-functional collaboration while balancing hands-on development and people leadership to meet product and business goals.

Serve as the primary product contact embedded with health system clients to discover workflows, design AI-powered solutions, scope pilots, and lead 0→1 deployments. Translate clinical and administrative workflows into product requirements, drive UAT and go-live, measure business success metrics, and surface field insights into the product roadmap. Travel to client sites for discovery and deployment support and collaborate with ML engineers, FDEs, and solution teams to ensure measurable ROI and expansion.

Build and productionize customer-facing data and AI solutions on the Databricks platform. Own architecture and design decisions, develop end-to-end data pipelines and applications, integrate machine learning models, and guide enterprise customers through implementation and adoption. Collaborate with engineering, product, support, project managers, and customer stakeholders while delivering projects, documenting solutions, resolving issues, and creating reusable frameworks. Travel to customers approximately 20% of the time.