Top Machine Learning Jobs in Tampa, FL

Lead development of cloud and on-premises data, ETL, analytics, and machine learning solutions. Responsibilities include building scalable data pipelines with AWS services, deploying serverless applications, automating infrastructure with Terraform, developing fraud detection models, and implementing supervised, unsupervised, reinforcement, and deep learning systems. The role also involves model monitoring, data governance, security, compliance, cost optimization, and integration with enterprise analytics and business intelligence platforms.

Leads New York Life Direct’s AI enablement strategy, multidisciplinary team, and enterprise AI portfolio. Oversees agentic, generative, predictive, and machine learning solutions from discovery through production, monitoring, adoption, and optimization. Establishes architecture patterns, engineering practices, governance, responsible AI controls, evaluation frameworks, and success metrics. Partners with business, technology, security, data, cloud, and risk teams to deliver scalable AI capabilities and measurable business value while developing talent and driving organizational adoption.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Lead design and implementation of AI and blockchain-enabled integration and platform architectures. Analyze requirements, develop technical designs, direct large projects, validate outcomes with clients, coach diverse teams, drive cloud strategy (Azure), perform feasibility studies and business process modeling to optimize enterprise technology infrastructure.

Lead and manage data science teams to build ML and deep learning models, maintain data pipelines and lakes, ensure data quality, deliver actionable insights, and manage project timelines and client expectations.

Supports digital assurance and audit teams by evaluating financial reporting, applying audit and compliance methodologies, conducting research, and assisting with cloud engineering, AI, machine learning, and data analysis initiatives. The intern learns PwC practices, contributes to client support, and collaborates on projects promoting transparency, governance, and regulatory compliance.

Leads commercial and prepaid card fraud strategy, detection optimization, risk controls, analytics, and performance monitoring. Manages fraud strategy analysts and partners with Product, Technology, Operations, Data Science, card networks, and external providers. Oversees strategy implementation, governance, testing, release management, root-cause analysis, and executive reporting while balancing fraud losses, client experience, operational efficiency, and business objectives.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Design, build, deploy, and support scalable Java microservices on AWS. Provide technical leadership, architectural direction, code reviews, troubleshooting, proof-of-concept development, and cross-functional collaboration. Work with high-throughput data, messaging systems, distributed architectures, observability, and AI-assisted development tools. Participate in on-call support, mentor engineers, lead architectural initiatives, and deliver reliable SaaS platform features.

The Senior Data Scientist supports Customer Success through deep-dive analysis, predictive modeling, causal inference, experimentation, and KPI development. The role analyzes churn, retention, renewals, upsell opportunities, and revenue trends; diagnoses metric shifts; builds data pipelines and dashboards; and develops AI/LLM-powered solutions. The scientist collaborates with stakeholders, communicates insights, improves data quality, and measures the impact of customer success initiatives and product changes.

Own the strategic vision and roadmap for Coursera’s search experience, including conversational, personalized, semantic, multilingual, and AI-driven discovery. Partner with engineering, design, research, data science, marketing, and product teams to improve search accuracy, conversion, and learner engagement. Lead experimentation, KPI measurement, competitive analysis, and revenue-focused personalization initiatives across a global learning platform.

Leads independent oversight of operational, technology, cybersecurity, and AI/ML risks across the firm. Evaluates AI/ML platforms and use cases, performs thematic risk analysis, challenges first-line mitigation strategies, assesses threat exposures and controls, and collaborates with technology and risk stakeholders to support resilient, responsible AI adoption.

Lead technical client onboarding, migrations, solution design, integrations, data orchestration, troubleshooting, and project delivery for Movable Ink’s AI-powered personalization platform. Partner with clients and internal teams to implement custom workflows, APIs, webhooks, and data solutions. Conduct training and workshops, provide product expertise, advise on roadmap improvements, and support long-term client success across email and mobile marketing programs.

Product Counsel will advise product, engineering, data science, marketing, compliance, and sales teams throughout the product lifecycle. Responsibilities include counseling on regulatory compliance, privacy, intellectual property, consumer protection, AI and machine learning, data rights, platform architecture, APIs, identity and account security, and marketing. The role translates legal requirements into technical guidance, supports AI governance, monitors evolving laws, and develops scalable legal processes for a fast-paced technology company.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Leads high-visibility product programs focused on market adoption, analyst relations, customer advisory boards, executive briefings, and industry events. Translates technical product roadmaps into compelling market value propositions, synthesizes analyst and customer feedback, and provides actionable insights to product leadership. Partners cross-functionally with Product, Marketing, and executive stakeholders while supporting strategic initiatives and external-facing communications.

Leads technical customer engagements for strategic enterprise accounts, combining solution engineering, software implementation, consulting, and sales support. Responsibilities include discovering requirements, designing and documenting SailPoint solutions, conducting foundational implementations and proof-of-value engagements, educating customers on architecture and best practices, and developing AI-enabled demonstrations and agentic solutions. The role collaborates across sales, product, professional services, partners, and customer success, and may require up to 15% travel.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Develops regulatory compliance analytics and reporting solutions using SQL, dashboards, governed metrics, semantic layers, and AI-enabled business intelligence. Builds and maintains Tableau or similar dashboards, automates reporting with Tableau and Alteryx, evaluates Machine Learning and GenAI opportunities, supports prompt development, and translates stakeholder needs into analytical products. Collaborates with Compliance, Risk, Technology, Finance, and senior governance leaders to improve regulatory engagement insights, adoption, and firm-wide decision-making.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.

Ten-week Advanced Analytics internship supporting data querying, data preparation, exploratory analysis, statistical modeling, machine learning, test design, and proof-of-concept development. The intern will apply AI and generative AI tools responsibly, evaluate analytical outputs and model performance, collaborate across business functions, complete analytics projects, and present findings to stakeholders.

Leads Toast’s AI and analytics strategy for Enterprise Care. Owns the roadmap, evaluates and deploys AI-assisted support tools, develops operational intelligence products, and partners with Product, Engineering, and Data teams to move pilots into production. Establishes complexity and workload intelligence, assesses AI readiness, translates AI performance into measurable operational outcomes, and builds organizational AI literacy. The role requires extensive enterprise support or service-delivery leadership, hands-on production AI experience, operational data fluency, and the ability to drive trusted adoption among agents and executives.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Own and improve Headway’s privacy program across HIPAA, 42 CFR Part 2, state privacy laws, individual rights, incidents, vendor governance, and AI use. Maintain controls, data maps, metrics, training, and operational workflows. Conduct product, vendor, and AI privacy assessments; manage rights requests and incident investigations; coordinate notifications and regulatory submissions; and translate complex requirements into practical guidance for cross-functional teams.