Top Machine Learning Jobs in Tampa, FL

Short-term (8-12 week) contractor role to translate SOX control testing judgment into AI-assisted workflows. Co-develop and prompt AI agents, run and evaluate control tests, document failure modes, design evaluation criteria, and partner with engineering to improve agentic testing.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Own and execute multi-year strategic partnerships with a hyperscaler and an AI lab, aligning technical integrations, commercial motions, and joint go-to-market plans. Coordinate cross-functional teams, run operational cadences, track measurable outcomes (pipeline, ARR, integrations), and represent partnerships to executives while driving hands-on execution and follow-through.

Lead AMP's multidisciplinary engineering organization to define and execute technology vision and roadmap. Responsible for scaling teams across hardware, software, AI, and infrastructure; driving product development, quality systems, and reliable deployments; partnering with execs and cross-functional teams on budgets, prioritization, and metrics; recruiting and mentoring leaders; and balancing short-term delivery with long-term investments to create business value.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

Support risk and analytics efforts by performing SQL/Python-based analysis, monitoring portfolio health, building scalable reports, collaborating cross-functionally, and integrating AI tools to surface insights that inform product, risk, and growth decisions.

Senior management role leading multiple application development teams to design, build, and operate scalable, cloud-native, and AI-enabled software solutions. Drive architecture, coding standards, DevOps/CI-CD, microservices, data platforms, and adoption of Agentic AI and ML. Manage budgets, hiring, performance, stakeholder relationships, risk and compliance, and mentor engineering leaders across distributed teams to deliver resilient enterprise applications.

Drive fraud strategy, policy, and analytics for money-movement fraud. Build and manage fraud rules, analyze moderately complex data sets using SQL/Python/SAS and BI tools, create predictive models and visualizations, consult stakeholders, and ensure fraud risk is identified and controlled across payment channels.

Lead MarTech strategy and hands-on execution for AI-driven, real-time 1:1 personalization. Evolve and evaluate MarTech stack, integrate AI/ML decisioning, build real-time journeys in Adobe Experience Platform, define XDM schemas and identity resolution, monitor dataflow health, lead Agile delivery pods, translate strategy to technical requirements, and create executive-level measurement and governance frameworks.

Lead applied AI/ML initiatives, design and deliver production-grade software, develop secure high-quality code, troubleshoot, automate remediation, evaluate vendor architectures, champion best practices across engineering, and drive adoption of AI/ML, NLP, LLMs and streaming technologies.

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Develop server-side Java applications using Spring MVC and RESTful services. Work with NoSQL (Cassandra), distributed caches (Hazelcast, Ignite, Redis), JVM performance tuning, modern JVM languages (Groovy/Scala), NLP/ML, and search platforms (Solr/Elasticsearch). Contribute to high-volume systems and open-source projects within agile teams.

Design and architect data solutions and large-scale Java/J2EE systems. Define extensibility, scalability, availability and security requirements. Document current and target state, gather functional and non-functional requirements, recommend process optimizations, and consider impacts of existing infrastructure and networks.

Lead proactive, hypothesis-driven threat hunts using SIEM/EDR/log data to identify adversary activity. Design and maintain hunt playbooks, analyze network and system logs, collaborate with SOC/IR/Red Team, present findings to leadership, and mentor other security staff to strengthen detection and response.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Lead Business Analyst partnering with wealth management stakeholders to define, document, and deliver technology solutions. Drive requirements, user stories, process models, UAT, and AI-enabled use cases while collaborating with product, engineering, QA, and data teams. Support production triage and improve processes using AI, automation, and analytics.

Independently validate and monitor machine learning models for credit underwriting, credit risk, and fraud detection. Identify model weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Support and maintain the Model Risk Management framework and coordinate with Internal Audit, Internal Controls, and Compliance on audit and regulatory requests.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

Design, develop, operationalize, and maintain enterprise-scale data applications and relational databases. Build scalable ETL/ELT pipelines, parse structured and unstructured data, and support data platforms on public cloud. Train, test, deploy, and maintain ML models, collaborate with interdisciplinary teams, and support mission-focused analytics for military and intelligence customers.

Design, build, and deploy scalable AI and ML solutions by wrangling and integrating data, implementing models (Python/Java, TensorFlow, Scikit-Learn), developing data pipelines and NLP applications, collaborating with clients, and mentoring junior team members to drive business insights and regulatory risk solutions.

Design, build, and deploy AI and machine learning solutions at scale. Develop data pipelines and scalable models, perform data wrangling and analysis, apply NLP and neural networks, collaborate with clients and teams, and contribute to open-source and iterative improvement of AI systems.

Lead technical direction and deliver large-scale ML platform infrastructure including feature store, model serving, experimentation, and training pipelines. Drive architecture, mentor engineers, coordinate cross-team initiatives, resolve system-wide issues (latency, reliability, GPU utilization), and define safe ML deployment paths from registration through rollout, monitoring, and rollback.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.