Top Senior Machine Learning Jobs in Tampa, FL

Drive new business and expand accounts by selling AI-driven services and software to engineering and manufacturing customers. Manage full sales cycle from prospecting to close, align solutions to measurable ROI, collaborate with technical and marketing teams, and meet revenue targets while attending industry events and maintaining CRM pipeline accuracy.

Lead ML feature strategy and engineering for fraud detection, owning end-to-end pipelines, validating features at scale, and translating analytics into production ML systems. Drive tooling and architecture decisions, mentor junior team members, partner cross-functionally with product and risk teams, and apply practitioner-level ML knowledge to detect and prevent account takeover and scam activity.

Lead a consortium advancing AI-driven neurodegeneration research: set strategy, build partnerships, manage multidisciplinary executive team, oversee product development and deployment, secure funding, ensure ethical AI and scientific integrity, and scale the platform to broader biomedical domains.

The AI Engineer will design and build production systems, focusing on LLM integration, system reliability, evaluation frameworks, and high-quality product features while maintaining strong engineering practices.

The role involves developing and improving TTS/Voice Generation models, training, fine-tuning, evaluating speech models, and collaborating across research, engineering, and product teams in a fast-paced startup environment.

Lead a team of machine learning engineers and data scientists to develop algorithms for personalized shopping, enhancing client engagement and driving growth through AI and data science.

The Business Development Director will lead strategic engagement with Google Cloud, focusing on partnership growth, go-to-market strategies, and revenue generation through cloud solutions.

The Business Development Director will lead strategic engagement with Oracle Cloud Infrastructure, focusing on alliance strategies, go-to-market execution, and revenue growth through innovative cloud solutions.

Lead the Prediction and Planning teams at Motional, overseeing machine learning initiatives, establishing technical roadmaps, and mentoring engineers. Publish research and collaborate across teams to integrate developments into vehicle platforms.

Lead vision, roadmap, and cross-functional teams to build AI-driven skilling products. Define success metrics, prioritize investments, ship LLM-powered features, measure learning outcomes, and drive adoption through partner and go-to-market collaboration.

Sell ExtraHops network security analytics to U.S. Federal/Department of War customers by developing territory accounts, engaging senior federal decision-makers, driving new business and renewals, coordinating with resellers and engineers, delivering tailored demos, and executing strategic account plans to meet revenue targets.

Serve as primary post-sales strategic partner for largest enterprise clients, driving AI Agent adoption, ROI, optimization, governance, renewals, and cross-functional advocacy to turn customers into champions.

Lead and grow a team of 5-7 enterprise sales reps in the Bay Area, recruit and develop sales leaders, build business plans, drive new-logo acquisition and account expansion, manage the sales cycle from lead generation to close, develop GTM strategy, and report to the VP of Sales. Role supports ambitious quarterly and annual goals and may require occasional work outside standard hours.

Own marketing measurement, operations, and analytics: build and QA campaigns, set goals, measure performance and attribution, manage data infrastructure and segmentation, optimize lead scoring/routing, apply AI/ML for automation and predictive insights, and document scalable processes to drive demand generation and cross-team alignment.

Contribute to building and scaling ML development infrastructure for Copilot: design and optimize CI/CD for large-scale training, build developer tools, maintain multi-cloud infrastructure, and deploy/host models for inference and data generation while collaborating with cross-functional teams to improve reliability, performance, and model development workflows.

Lead customer-facing data science engagements: manage data acquisition and cleaning, build and evaluate scalable models, drive model best practices and ethics, write extensible code, research industry trends, define solution strategy, and collaborate across engineering and product teams to deliver AI/cloud solutions.

Lead statistical design and analysis for Phase I-IV clinical studies, develop protocols and SAPs, perform efficacy and exploratory analyses, run simulations, apply advanced methods (mixed models, longitudinal), oversee datasets (ADaM/CDISC), use R and SAS for analyses, and provide strategic statistical guidance to cross-functional teams supporting regulatory submissions.

Lead requirements discovery and delivery for Wealth Management technology initiatives, translating business needs into technical requirements, user stories, process flows and AI-enabled solutions. Facilitate workshops, support UAT and production issue resolution, collaborate with cross-functional teams, and drive AI/automation opportunity identification and adoption.

Drive supply chain efficiency through data analytics, KPI development, process redesign, ERP/WMS/automation selection and implementation, Lean Six Sigma continuous improvement, AI-driven forecasting, cross-functional project leadership, audits, and change management with suppliers and 3PLs.

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

Lead product strategy and execution for a B2B SaaS marketing analytics platform. Translate research into requirements, own the backlog, partner with engineering and GTM, define success metrics, and ship API- and data-heavy features for technical users.

The Senior Director of AI Insights leads AI-driven data insights to enhance decision-making and strategy alignments across the organization, fostering stakeholder engagement and AI literacy.

The role involves cultivating technical relationships with Accenture, supporting solution development, customer deal support, and representing Databricks at events.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.