Top Machine Learning Jobs in Cleveland, OH

The Senior Generative AI Engineer will design and deploy AI/ML solutions, focusing on Generative AI systems and their operational excellence across various sectors like R&D and Clinical Trials.

Design, build, and deploy production-grade AI/ML and LLM-based systems. Lead end-to-end ML lifecycle, implement RAG and embedding pipelines, establish MLOps best practices, mentor engineers, and collaborate with cross-functional teams to deliver scalable, responsible AI solutions.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.

Design, research, and deliver LLM and GenAI solutions for healthcare SaaS at scale: architect systems, handle healthcare data, implement PEFT and RAG pipelines, ensure responsible AI, mentor teams, and translate technical outcomes to business impact.

The role involves improving AI agent quality through designing experiments, maintaining evaluation infrastructure, optimizing LLM inference, and collaborating with product teams. Responsibilities include end-to-end ownership from research to deployment in a dedicated ML Engineer context.

The Lead Machine Learning Scientist will develop and implement ML strategies to enhance marketing and product capabilities, identify opportunities for growth using data, lead core model development, and improve marketing spend efficiency through insights and experimentation.

Design and operate production ML systems for large-scale streaming data, ensuring reliability and performance while collaborating with researchers to integrate models into distributed systems.

Design, develop, and deploy machine learning models; analyze large datasets; build scalable data pipelines and distributed computing environments; integrate ML solutions with cross-functional teams; optimize performance and support production ML infrastructure.

Design, build, and productionize scalable AI systems for autonomous detection, RAG-backed investigations, and auto-remediation. Develop LLMs, graph-based reasoning engines, streaming pipelines, evaluation/red-team processes, and collaborate with cross-functional teams to translate attacker behavior into robust detections.

Lead design and delivery of scalable AI systems for autonomous threat detection and response. Build and productionize LLMs, graph reasoning engines, streaming feature pipelines, and orchestration frameworks. Own evaluation, reliability, and cross-functional collaboration while mentoring engineers and experimenting with RAG, tool-calling agents, and multi-modal models.

Lead assessment, redesign, and hands-on implementation of data pipelines, ML workflows, and platform infrastructure. Produce prioritized system designs, harden production pipelines, improve observability, reliability, and data quality, and mentor the data team.

The Machine Learning Engineer will design and build systems that improve the AI shopping agent by using real user feedback, focusing on feedback loops and metrics to enhance user interactions and agent performance.

Design, build, and deploy scalable ML models and pipelines to enhance a talent marketplace and GenAI solutions. Lead experimentation (A/B tests, statistical validation), improve production ML performance, ensure code and data quality, work with cross-functional teams, mentor junior engineers, and propose innovative AI-driven solutions aligned to business goals.

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.

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 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.

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.

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.

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.