Top Machine Learning Jobs in Cincinnati, OH

The job involves selling AI and Machine Learning technologies to mortgage companies, managing the sales process for enterprise customers, and working with remote teams.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

Lead the integration of AI and digital solutions in manufacturing processes, driving adoption and measurable impact on efficiency, quality, and performance.

The Staff Machine Learning Engineer will lead AI initiatives, architect LLM systems, design evaluation frameworks, and mentor engineers.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

As a Principal Solutions Architect, you will oversee technical architecture for VA IT modernization, writing prototypes, guiding engineering teams, and representing Credence to VA stakeholders. You will also contribute to capture strategy and mentor engineers on compliance and integration.

The Principal Product Manager - Growth will lead growth initiatives, focusing on user engagement, retention, and the use of AI to enhance product strategies and experiences.

Design, build, and deploy machine learning systems for controlling quantum processors. Develop reinforcement learning and Bayesian methods for QEC calibration, and work cross-functionally with product and R&D teams.

As a Principal Product Manager, you will drive innovation in AI technology, reshape expert acquisition strategies, and collaborate with cross-functional teams to enhance product offerings.

The Senior Technical Architect will lead AI SDLC transformations, create architectural solutions, mentor teams, and drive innovation in regulated industries.

As a Regional Sales Director, you'll lead a sales team, develop business plans, and drive sales growth by managing complex cycles and nurturing talent.

Lead end-to-end product strategy and delivery for a core marketplace area, collaborating with engineering, UX, and design. Conduct qualitative and quantitative research, run agile sprints, measure results with analytics, and pioneer AI/ML product features and workflows to improve user experience and platform impact.

Lead go-to-market for Mapboxs AI products: define positioning, messaging, and launch strategy; drive customer research, sales enablement, content, and cross-functional alignment to accelerate adoption of AI-powered location capabilities.

Lead document-vision research and production systems for multimodal legal evidence understanding. Define evaluation standards, frame hard problems, design and ship models, run efficacy studies and monitoring, mentor senior scientists, and advise leadership on scientific direction.

Lead development and application of analytics, statistical models, and dashboards to support R&D Quality oversight. Integrate clinical, safety, lab, and operational data to identify risks, support audit/inspection readiness, enable RBQM, and provide actionable quality insights and recommendations to cross-functional partners.

Lead and grow a 9-10 engineer cross-functional team to own AI product areas (Quote Agent, Submission Triage, Product Studio). Define architecture for agentic/LLM systems, ensure engineering quality, partner with Product/Design/GT M stakeholders, and drive delivery of scalable, production AI features while fostering ownership and continuous growth.

This role leads the ML Acceleration team, focusing on enhancing ML model training efficiency and team management, while driving technical strategy and collaboration.

Lead strategic vision and execution of next-generation bioinformatics technologies. Identify and evaluate emerging computational methods, translate research into production-grade pipelines and prototypes, support product roadmaps and technology transfer, and partner cross-functionally to commercialize genomic and AI-driven healthcare solutions.

Lead combined Analytics Engineering and Data Engineering teams owning the end-to-end data roadmap from ingestion to trusted metrics. Manage hiring and performance, set architecture and platform investments, enforce testing/monitoring/documentation standards, review technical decisions, and partner with product and business stakeholders to translate needs into prioritized deliverables.

Lead pricing and monetization strategy across global markets. Design pricing models and packaging, optimize discounting and payments economics, apply AI/ML analytics to drive revenue and margin, partner cross-functionally with Product, Sales, Finance, and Revenue Operations, govern pricing changes, and enable sales with pricing tools, policies, and training to improve ARR, retention, and profitability.

Analyze historical demand, occupancy, transactions and competitor pricing to optimize daily, monthly, and event pricing. Build dashboards, deliver revenue insights to leadership, and collaborate with Operations and Marketing to implement pricing and promotional strategies while maintaining data integrity.

Provide senior legal and regulatory counsel for digital health and medical device products (SaMD), guiding product lifecycle, FDA and global filings, AI-enabled features, labeling, compliance, post-market obligations, and cross-functional governance. Lead strategy, manage external counsel, and translate regulatory developments into business guidance.

Develop and productionize AI/ML systems across the full lifecycle: design agentic workflows, integrate models into applications, implement multi-agent orchestration, build data pipelines, write scalable code, monitor and optimize production AI, and collaborate with cross-functional teams.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Build and scale the ML infrastructure platform for humanoid robotics, including data processing, training, evaluation, deployment, model management, observability, and reproducible workflows. Develop cloud-native Kubernetes workflows, model registries, feature stores, experiment tracking, developer APIs, CLI tools, and ML CI/CD systems. Collaborate with AI researchers, robotics engineers, and data platform teams while mentoring junior engineers and shaping platform architecture, reliability, and scalability.