Top Senior Machine Learning Jobs in Madison, WI

Lead design, build, and deploy scalable, reliable Java-based microservices in a cloud-native, event-driven AWS environment. Own backend feature design, code quality, automated testing, and FedRAMP release processes. Mentor engineers, participate in on-call/TechOps, integrate AI tools into development, and drive architecture, observability, and cross-team initiatives.

As a Customer Success Engineer, you'll drive adoption and satisfaction for enterprise customers using Deepgram's voice AI technology, fostering strong relationships and aligning technical solutions with business objectives.

Lead and manage the underwriting and credit risk model validation team, perform hands-on independent validations of complex ML models, assess data integrity and performance, identify and remediate model weaknesses, partner on MRM framework improvements, and coordinate with audit, compliance, and controls to meet regulatory standards.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Lead and transform the Fraud Intelligence Team by designing scalable operating models, QA and measurement frameworks, and talent programs. Drive labeling quality, operational metrics, and fraud prevention impact; convert frontline fraud observations into insights for Product, R&D, Analytics, and GTM; leverage AI, automation, and tooling; and serve as a cross-functional fraud subject-matter expert while developing early-career talent and measurable business outcomes.

Conduct hands-on molecular and synthetic biology research to engineer bacteriophages. Responsibilities include construct design and assembly, cloning, engineered phage testing, host-phage assays, troubleshooting experimental workflows, improving protocols, and generating high-quality data for machine learning model development and therapeutic research.

Lead design and implementation of Headway's insurance systems (eligibility, pricing, matching, claims). Build data-intensive, ML-powered platforms and automation, mentor engineers, write high-impact code, and improve accuracy and reliability across state and payer complexity to scale insurance-covered therapy.

Tenure-track, open-rank faculty position in Learning Sciences integrating artificial intelligence. Responsibilities include establishing a visible research program, publishing, securing extramural funding, mentoring graduate students, teaching graduate/undergraduate courses, and contributing to departmental, school, and university service. Research may focus on generative AI, LLMs, multimodal models, human-AI interaction, CSCL, and ethical implications of AI in education.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

Design, build, and operate hyperscale Azure availability and control-plane services to achieve high VM SLAs. Lead architecture, author designs, implement reliable code, run livesite as DRI, and integrate predictive ML/AI models and generative AI for diagnostics and automated root-cause analysis.

Lead development of Root’s machine learning platform for insurance pricing. Build and operate feature pipelines, model training and orchestration, registries, validation, serving, observability, and reproducibility tooling. Partner with researchers to convert experimentation into reliable production systems, explore LLM-enabled data science automation, drive technical roadmaps and execution, and mentor engineers while maintaining high standards for reliability and correctness.

Coordinates data platform incident response, triage, escalation, root cause analysis, problem management, post-incident reviews, and operational improvements. Maintains runbooks, documentation, metrics, dashboards, and stakeholder communications. Implements and monitors security controls, supports security incident response, vulnerability remediation, compliance audits, regulatory reporting, disaster recovery exercises, and cross-functional coordination across data, cloud, infrastructure, DevSecOps, application, and governance teams.

Develop and deploy predictive and causal models for personalization, retention, monetization, and product strategy. Design experimentation and measurement frameworks, analyze user and marketing behavior, quantify business impact, and translate complex findings into strategic decisions. Collaborate with Product, Engineering, Marketing, and Data Product teams to build scalable data solutions. Mentor peers, promote rigorous model validation and governance, and apply Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to production-ready data science initiatives.

Leads strategic, full-cycle technical recruiting for Engineering, including software, cloud, AI/ML, data, cybersecurity, quality, and product roles. Partners with senior engineering leaders on workforce planning, hiring strategies, skills-based criteria, sourcing, structured interviews, and talent pipelines. Uses market intelligence and recruiting analytics to improve hiring quality, candidate experience, and time-to-fill while coaching hiring managers and driving continuous process improvement.

Lead the design and implementation of large-scale AI and machine learning systems for Yahoo Search across multiple teams. Define data strategies, evaluation frameworks, and scalable production-serving approaches. Drive technical alignment among engineering, product, design, and research teams; guide adoption of large language models, vision models, generative AI, and agentic AI; and oversee data analysis informing strategic product decisions.

Drive enterprise sales growth across North American banking, credit card, and financial services accounts. Develop strategic account and business development plans, build customer and partner relationships, close complex multi-million-dollar technology deals, and engage C-level stakeholders. Represent the company at industry events, collaborate with commercial and leadership teams, manage pipeline forecasting and reporting, and apply MEDDPICC methodology. The role focuses on cybersecurity, fraud prevention, authentication, and SaaS or cloud solutions, with 50% domestic and international travel.

Own an AI stockbroking product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, reconciliation, security, scalability, and failure handling. Embed KYC, transaction monitoring, limits, privacy, and licensing requirements. Apply AI with evaluation, safeguards, human oversight, and reliability controls while coordinating Engineering, Design, Compliance, Operations, Treasury, and Finance.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Lead the design, development, deployment, and governance of advanced analytics, machine learning, and AI solutions using large healthcare datasets. Responsibilities include data preparation, statistical and predictive modeling, model monitoring, explainability, visualization, stakeholder communication, and translating findings into clinical and operational improvements. The role also mentors data professionals, advises leaders, manages initiative priorities and risks, and promotes responsible AI and Agile practices.

You will tackle AI challenges, develop scalable software and collaborate with teams to enhance healthcare navigation using AI solutions.

Lead and transform the Fraud Intelligence Team into a high-impact function by improving operating models, QA, tooling, and metrics; build entry-level talent programs; connect labeling to measurable fraud prevention outcomes; deliver actionable insights to Product, R&D, Analytics, and GTM; and leverage AI and automation to increase productivity and business impact.

Partner with functional leaders to translate business strategy into people strategy. Own org design, performance management, compensation, talent development, engagement, and retention. Provide coaching on career pathing, change management, and sensitive employee issues. Use people data to generate insights and recommendations, build compliant HR policies, manage global employee relations, and collaborate with People Team COEs to deliver cohesive people programs at scale.

Serve as second-in-command for Coinbase Derivatives' Market Regulation Department, leading day-to-day regulatory compliance, managing exams and audits, advising on CFTC Core Principles, reviewing surveillance escalations and enforcement actions, and coordinating cross-functional regulatory workstreams to ensure the DCM meets obligations and resolves findings timely.

Own end-to-end client relationships for a book of B2B SaaS customers: drive adoption, engagement, retention, and expansion of AI-powered contract drafting tools. Lead onboarding, training, large-scale rollouts, and translate customer feedback into product improvements while designing scalable CS processes and tracking health metrics to demonstrate value.