Top Machine Learning Jobs in Cincinnati, OH

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

Lead a team to drive data-informed clinical growth and market expansion through market planning, forecasting, AI/ML-enabled analytics, competitive intelligence, risk simulation, data governance, and cross-functional strategic advisory to senior leadership.

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.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

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.

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.

Oversee global analytics and algorithm design initiatives for a nonprofit: identify high-impact projects, coordinate with student directors, collect feedback, design and run training for developers and technical advisors, and form partnerships to support implementation. Volunteer commitment ~5-6 hours/week.

The Product Marketing Manager will execute product launches and support go-to-market initiatives, collaborating with cross-functional teams to develop and deliver messaging and enablement materials targeting both technical and business stakeholders.

The role involves conducting research and optimizing machine learning systems while collaborating with technical staff to enhance product performance.

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.

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 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.

The Senior Data Scientist will manage data collection, design study protocols, train juniors, lead report development, and support innovative analytics tools.

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.