Top Machine Learning Jobs in Cincinnati, OH

As a Manager of Cloud Software Engineering at Coupa, you will lead AI platform DevOps, manage global teams, and oversee agile delivery while ensuring technical quality and innovation.

The Senior Software Engineer will design and optimize recommendation algorithms, maintain data pipelines, and improve personalized user experiences using machine learning techniques and backend engineering skills.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

As a Principal Software Engineer, you'll architect and code backend services and ML models, integrate them with Salesforce, and mentor junior engineers.

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.

The AI & Mobile Product Manager will lead the strategy and delivery of AI-powered mobile solutions, managing the product lifecycle from concept to adoption, collaborating with various teams to ensure product alignment with business objectives, compliance, and customer satisfaction.

Lead strategy and delivery for an AI-powered claims decision-support and auto-adjudication platform. Define vision and roadmap, manage a team of product managers, partner with business, engineering, and data science, and drive adoption, impact measurement, and executive-level communication across a complex, matrixed enterprise.

Own the end-to-end analytics product delivering trended insights and AI-driven recommendations. Define vision, roadmap, and backlog; lead pilots, validate in-field value, scale deployments, support commercialization and adoption, own business case and metrics, mitigate technical/operational/commercial risks, and drive evolution toward machine-learning insights.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

This is a general talent-community application inviting candidates across many functions to submit resumes. Recruiting will review and retain applications for future openings; candidates should highlight experience, accomplishments, and interests for potential remote roles.

Conduct ML research for query understanding, ranking, clustering, and AI-powered search. Prototype and productionize algorithms, define data and evaluation requirements, design scalable systems, collaborate with engineers, run experiments, and publish patents/papers to integrate state-of-the-art ML into Yahoo Search products.

Own OEM-facing product roadmap for the Automotive Enterprise Cloud, translate OEM requirements into scalable integrations (APIs, data pipelines, reporting), lead discovery and delivery with cross-functional teams, define success metrics, produce product artifacts, and drive OEM adoption and satisfaction.

This role involves leading strategic planning, product innovation, and cross-functional collaboration to enhance pharmacy analytics and drive growth initiatives for CVS Health.

As Director of AI/ML, lead the ML organization, set technical vision, mentor a high-performing team, and contribute to model development and production deployment.

The Senior Director of Data Science & AI leads AI strategy execution, managing a multidisciplinary team to develop and govern AI solutions, with a focus on innovation, operations, and ethical standards.

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.

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.

Contract Technical Recruiter (12-month, hybrid in Austin) partnering with engineering leaders to source, attract, and hire AI/ML, systems software, and hardware engineering talent. Build hiring strategies, talent pipelines, and targeted sourcing campaigns, deliver a high-touch candidate experience, and share market intelligence to improve recruiting outcomes.

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

Lead development of an AI-enabled Ethereum Layer 2 platform: build front-end and back-end systems, deploy and optimize AI models, create data pipelines, collaborate on model training, and contribute to product innovation and other company projects.

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.