Top Remote Machine Learning Jobs in Madison, WI

Design, build, and modernize telecom OSS/BSS and 5G core components using cloud-native microservices, Kubernetes, and real-time streaming. Implement API-first integrations (TM Forum Open APIs), CI/CD, Helm/Istio, and production ML for predictive maintenance and fraud detection. Manage legacy migrations, multi-vendor integrations, edge/MEC and network-slicing orchestration, and enforce zero-trust security and SBOM practices to ensure resilient, scalable network operations.

Lead end-to-end autonomy programs from concept to field deployment across agriculture and construction. Create and optimize plans, manage cross-functional stakeholders, mitigate risks, track outcomes, remove impediments, and ensure alignment with product, safety, and operational goals.

Lead a team to develop groundbreaking technology combining NLP, big data, and AI to enhance digital communication and interaction.

The AI & Data Science Director will lead AI strategies, foster technical leadership, support team development, and integrate generative AI into products.

Lead the Client Solutions team, ensuring the execution of complex transactions while driving innovation and client advocacy. Oversee operations, build client relationships, and implement strategic frameworks for growth.

As a Data Scientist, you will build predictive models, develop software tools, and analyze geoscientific data to support mineral exploration and discovery.

The Data Scientist will develop predictive models, analyze geophysical data, and collaborate with geoscientists to enhance exploration programs, focusing on mineral deposits.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

The Forward Deployed Engineer collaborates with clients to solve complex problems using technology, integrating systems, writing production code, and leading projects to deliver impactful solutions in areas like financial crime prevention and national security.

Develop and deploy end-to-end machine learning solutions, collaborate with cross-functional teams, and implement MLOps frameworks while monitoring model performance in production.

The Director of Data Sciences leads a team in strategic initiatives, drives machine learning and AI solutions, and manages resources to enhance data-driven decision making across the business unit.

The GTM role focuses on increasing revenue through new sales and partnerships, understanding client needs, and communicating product value.

As a Sr Director Analyst, you will create actionable research in analytics and AI, advise clients, and lead a team in impactful research. Responsibilities include engaging clients, presenting insights, and mentoring team members in analytics & AI best practices.

The Senior Product Manager will lead the integration of machine learning into search relevance, defining product vision and collaborating with teams to enhance ecommerce search functionality.

The Data Science Actuary role involves building and maintaining rating systems and risk models, conducting analysis for underwriting and operations, and integrating new data sources while collaborating with multiple teams.

As Software Engineering Manager, you'll lead a team in enhancing content workflows through automation and AI, develop scalable solutions, and manage cross-functional collaboration.

Serve as the primary technical resource for channel partners to enable partner sales through training, demos, PoV engagements, joint account strategy, and creation of repeatable technical assets and reference architectures. Influence product roadmap via partner feedback and represent 7AI at events to scale partner-led pipeline and long-term customer success.

Lead client-facing AI program governance and delivery as PMO manager. Translate business objectives into AI roadmaps and OKRs, oversee AI/ML lifecycle, ensure data readiness, model validation, deployment, and responsible AI compliance. Manage PMO standards, portfolio prioritization, AI-augmented reporting, and lead executive communications while mentoring PMs, TPMs, and analysts.

Manage a portfolio of B2B SaaS customers as their primary contact to drive adoption, onboarding, migrations, engagement, and renewals. Deliver training, business reviews, issue resolution, and strategic expansion plans. Coordinate cross-functionally with sales, product, and support while reporting on customer health, usage metrics, and opportunities for upsell.

Lead the Data Sources product roadmap for document parsing, fraud detection, and bank aggregation. Drive metrics (parse accuracy, fraud catch rate, connection success, SLAs), write specs, engage clients, mentor PMs/engineers, and represent the platform externally. Prioritize document types, extraction models, reliability, and scalability to improve conversion, client satisfaction, and operational efficiency across mortgage, tenant screening, and government use cases.

Lead product strategy and roadmap for two flagship B2B SaaS solutions in Food Equipment Services. Drive discovery, prioritize AI-driven quoting and design features, partner with engineering and GTM teams, define success metrics, and oversee delivery, adoption, and continuous improvement.

Lead end-to-end post-sale value stream (implementation through renewal), design the I2R operating model, govern AI/agentic systems, drive retention and time-to-value, partner across Product/Finance/Engineering, apply SAFe and LPM to scale automation and measurable outcomes for adoption, churn reduction, and renewals.

Serve as technical lead in pre-sales for identity verification and fraud prevention. Lead discovery, workshops, POCs, demos, and solution design; advise on risk/compliance; align cross-functionally; influence product; mentor junior consultants and contribute thought leadership.

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.

This role involves designing, deploying, and maintaining machine learning and AI models in production, managing ML pipelines, and ensuring system reliability and performance.