Top Machine Learning Jobs in Cleveland, OH

Leads the enterprise Data Science and AI vision, strategy, roadmap, and global organization. Oversees production-grade machine learning, Generative AI, Agentic AI, predictive modeling, AI governance, deployment, monitoring, cost optimization, and responsible AI. Partners with executive, product, engineering, commercial, legal, operations, and customer success stakeholders to deliver scalable AI products, automation, improved customer outcomes, and measurable business value.

Designs and scales AI-driven security GRC automation, including compliance evidence collection, control testing, risk assessment, audit preparation, and workflow integration. Builds scripts, bots, APIs, and AI/LLM solutions; integrates GRC platforms with security and IT systems; establishes quality controls and guardrails; monitors automation risk and performance; and partners with technical and business stakeholders to improve security compliance maturity.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Own and scale Jellyfish’s security engineering program, including strategy, architecture, secure SDLC governance, AI security, threat modeling, vulnerability remediation, incident response, penetration testing, and bug bounty programs. The role combines hands-on software development with executive advising, cross-functional leadership, mentorship, and security risk management across SaaS infrastructure and customer-facing AI products.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Lead product strategy for AI-driven business automation and a monetized data-as-a-service offering. Build enterprise AI governance, agent operating models, evaluation standards, privacy controls, and responsible AI practices. Develop an AI champion network, prioritize automation opportunities, and measure productivity gains. Define data products, customer segments, pricing, packaging, and go-to-market plans while partnering across engineering, security, legal, privacy, risk, and business units.

Own post-sale relationships with referral partners, driving adoption, retention, renewals, expansion, and revenue growth. Serve as a strategic advisor, lead business reviews, resolve escalations, coordinate cross-functional teams, enable product usage, and advocate for partner needs. Maintain partner records and report on satisfaction, renewal, and revenue metrics while developing success strategies and playbooks.

Own the strategy, roadmap, requirements, launch, adoption, and success metrics for a technical platform. Partner with engineering, data, product, and business teams; evaluate architecture, APIs, and system tradeoffs; prioritize foundational investments against customer and revenue outcomes; and apply AI to improve platform capabilities. The role requires strong product management experience, technical depth, analytical skills, and production experience with LLM-based or ML-adjacent products.

Leads talent acquisition strategy for specialized AI, machine learning, robotics, automation, and advanced research roles. Sources and closes highly technical candidates, advises senior leaders on talent markets and compensation, mentors recruiters, builds research and industry relationships, and improves recruiting processes using analytics, recruiting technology, and Lean methods.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages sprint planning, releases, DevOps metrics, project delivery, and stakeholder communication. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, and ensures reliable, well-documented data solutions that support organizational reporting and analytics needs.

Manages a business analytics team that delivers insights, KPI reporting, trend analysis, data visualizations, governance, and actionable recommendations. Guides statistical analysis, data modeling, querying, experimentation, and self-service analytics while aligning practices with enterprise standards. Partners with leaders to identify opportunities and risks, improve data quality, drive analytics adoption, and support strategic and operational decisions. Oversees team performance, department projects, communications, and best-practice development.

Lead the design and development of Azure Compute Fleet capabilities that orchestrate Virtual Machine Scale Sets across regions. Build distributed control-plane services, APIs, and workflows for capacity placement, scaling, failover, and lifecycle management. Collaborate across Azure engineering teams, drive AI-enabled engineering improvements, participate in on-call operations, and improve reliability, observability, performance, and recovery for hyperscale cloud services.

Leads global people analytics and workforce intelligence strategy, building AI-driven tools, predictive models, dashboards, and decision-support applications. Partners with IT, data, and security teams on governance, privacy, and ethical AI. Advises executives, democratizes workforce insights, measures people-program ROI, and anticipates talent trends through benchmarking and workforce modeling.

The AI Engineer designs, develops, and operationalizes responsible AI systems, including generative AI applications, RLHF models, guardrails, prompt engineering, and RAG frameworks. Responsibilities include evaluating and fine-tuning models, developing Python applications on cloud platforms, implementing ML software engineering practices and CI/CD pipelines, and collaborating with stakeholders on AI roadmaps and delivery. The role requires leading complex AI/ML initiatives and translating technical concepts for diverse audiences.

Develop and validate machine learning and predictive models, perform statistical and data analysis, conduct ETL across repositories, and assess model quality. Communate findings through visualizations and written reports, automate analytical workflows, and collaborate directly with nontechnical customers to refine requirements and deliver data-driven solutions. The role requires expertise in ML/AI, programming, cloud environments, CI/CD, and government or safety-critical systems.

Leads business analytics engagements, translating stakeholder needs into data requirements, models, dashboards, forecasts, statistical analyses, and actionable recommendations. Performs data exploration, querying, modeling, and visualization across large datasets; supports data governance, quality, security, and stewardship. Mentors analysts, communicates insights to leaders, collaborates with enterprise data teams, and identifies opportunities to apply machine learning in business decision-making.

Develop and deploy UiPath-based RPA solutions for internal and external clients. Responsibilities include requirements gathering, process documentation, automation development, testing, deployment, queue management, scheduling, exception handling, debugging, governance, and Center of Excellence support. The role also involves business process analysis, system design, process optimization, change management, tool selection, and collaboration with developers, analysts, testers, and consulting teams.

Leads OuterBox’s internal AI strategy, AI engineering team, and development of client-facing AI services. Identifies market opportunities, converts internal capabilities into priced consulting offerings, oversees production deployments and partner programs, and drives AI adoption, training, and measurement. Partners with executives, clients, engineering, and technology teams on architecture, security, cloud governance, reliability, pricing, and ROI.

Leads Human Interest’s ERISA compliance program across 401(k) operations, ensuring adherence to ERISA, DOL, and IRS requirements. The role modernizes compliance workflows, develops automated monitoring and reporting systems, manages fiduciary governance and regulatory incidents, oversees escalations and remediation, partners with Product and Technology, manages auditors and regulators, and coaches compliance managers. The director also advises executives on risk, maintains policies and training, and scales technology-enabled compliance operations.

Own analytical relationships with travel technology partners, creating scalable reporting on supply, pricing, and performance. Conduct exploratory analysis, apply machine learning and statistical methods, build dashboards, automate recurring workflows with AI tools, and translate complex findings into recommendations for product, engineering, commercial, and executive audiences.

Build and deploy AI-enabled solutions for Marketplace operations and commercial challenges. Identify opportunities, frame ambiguous business problems, develop prototypes using enterprise data and AI/LLM tools, validate business value, and transition successful solutions into production. Partner with business, product, technology, and data teams; drive adoption, measure outcomes, coordinate initiatives, mentor team members, and enhance reusable platforms and frameworks.

Lead Quantiphi’s co-sell motion with frontier AI partners including OpenAI, Anthropic, and LangChain. Build partner-seller relationships, originate and qualify enterprise AI opportunities, coordinate multidisciplinary pursuit teams, manage account and pipeline reviews, and drive proposals and commercial closures. Translate customer AI adoption needs into implementation services engagements, support production deployments, and develop repeatable sales plays and expansion opportunities.

Build and deploy machine learning models for risk and fraud detection. Lead technical efforts in the risk and fraud domain, use SQL and Python for data preparation and production code, design statistically sound experiments, evaluate model performance, and partner with Operations to respond to evolving financial threats.

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Drive enterprise SaaS sales from prospecting through close, expanding adoption among strategic accounts. Engage business and technical decision-makers, including C-suite stakeholders, to address fraud challenges using Sift’s machine learning platform. Build consultative business cases, articulate platform differentiation, collaborate cross-functionally, and help shape sales strategy and growth. Requires strong organizational skills, adaptability, and experience managing complex B2B sales cycles.