Top Machine Learning Jobs in Tampa, FL

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

The Senior Customer Success Manager drives revenue growth by ensuring customer satisfaction, building strong relationships, managing renewals, and promoting product solutions aligned with client goals.

Lead the integration of AI and digital solutions in manufacturing processes, driving adoption and measurable impact on efficiency, quality, and performance.

The Staff Machine Learning Engineer will lead AI initiatives, architect LLM systems, design evaluation frameworks, and mentor engineers.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

As a Principal Solutions Architect, you will oversee technical architecture for VA IT modernization, writing prototypes, guiding engineering teams, and representing Credence to VA stakeholders. You will also contribute to capture strategy and mentor engineers on compliance and integration.

The Principal Product Manager - Growth will lead growth initiatives, focusing on user engagement, retention, and the use of AI to enhance product strategies and experiences.

Design, build, and deploy machine learning systems for controlling quantum processors. Develop reinforcement learning and Bayesian methods for QEC calibration, and work cross-functionally with product and R&D teams.

As a Principal Product Manager, you will drive innovation in AI technology, reshape expert acquisition strategies, and collaborate with cross-functional teams to enhance product offerings.

The Senior Technical Architect will lead AI SDLC transformations, create architectural solutions, mentor teams, and drive innovation in regulated industries.

As a Regional Sales Director, you'll lead a sales team, develop business plans, and drive sales growth by managing complex cycles and nurturing talent.

Lead end-to-end product strategy and delivery for a core marketplace area, collaborating with engineering, UX, and design. Conduct qualitative and quantitative research, run agile sprints, measure results with analytics, and pioneer AI/ML product features and workflows to improve user experience and platform impact.

Lead go-to-market for Mapboxs AI products: define positioning, messaging, and launch strategy; drive customer research, sales enablement, content, and cross-functional alignment to accelerate adoption of AI-powered location capabilities.

Lead development and application of analytics, statistical models, and dashboards to support R&D Quality oversight. Integrate clinical, safety, lab, and operational data to identify risks, support audit/inspection readiness, enable RBQM, and provide actionable quality insights and recommendations to cross-functional partners.

Lead and grow a 9-10 engineer cross-functional team to own AI product areas (Quote Agent, Submission Triage, Product Studio). Define architecture for agentic/LLM systems, ensure engineering quality, partner with Product/Design/GT M stakeholders, and drive delivery of scalable, production AI features while fostering ownership and continuous growth.

This role leads the ML Acceleration team, focusing on enhancing ML model training efficiency and team management, while driving technical strategy and collaboration.

Lead combined Analytics Engineering and Data Engineering teams owning the end-to-end data roadmap from ingestion to trusted metrics. Manage hiring and performance, set architecture and platform investments, enforce testing/monitoring/documentation standards, review technical decisions, and partner with product and business stakeholders to translate needs into prioritized deliverables.

Lead pricing and monetization strategy across global markets. Design pricing models and packaging, optimize discounting and payments economics, apply AI/ML analytics to drive revenue and margin, partner cross-functionally with Product, Sales, Finance, and Revenue Operations, govern pricing changes, and enable sales with pricing tools, policies, and training to improve ARR, retention, and profitability.

Analyze historical demand, occupancy, transactions and competitor pricing to optimize daily, monthly, and event pricing. Build dashboards, deliver revenue insights to leadership, and collaborate with Operations and Marketing to implement pricing and promotional strategies while maintaining data integrity.

Provide senior legal and regulatory counsel for digital health and medical device products (SaMD), guiding product lifecycle, FDA and global filings, AI-enabled features, labeling, compliance, post-market obligations, and cross-functional governance. Lead strategy, manage external counsel, and translate regulatory developments into business guidance.

Sell ExtraHop's NDR platform in an assigned territory by closing new accounts, expanding existing customers, managing channel/reseller relationships, forecasting, and positioning the product for AI-era SOCs. Develop account plans, present demos, gather field intelligence, and meet sales targets while traveling extensively.

Lead strategic direction, operations, and growth for Itemized Bill Review. Own product performance, automation/AI initiatives, global team leadership, compliance, KPIs, client engagement, and continuous improvement to drive savings and scalability.

Lead credit and funding decisioning for auto lending partners, exercising delegated lending and funding authority, resolving exceptions and stipulations, feeding performance insights to product and risk teams, conducting win/loss analysis, and enabling dealer-facing operations and training to protect portfolio performance and improve approval workflows.

Lead product management for AI tools in software/SaaS, manage product lifecycle, drive strategic roadmap, and ensure product adoption through iterative shipping and user feedback.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.