Top Machine Learning Jobs in Tampa, FL

The Sales Development Representative generates qualified pipeline through personalized phone, email, and LinkedIn outreach. Responsibilities include engaging decision-makers, qualifying inbound and outbound prospects, setting meetings, coordinating with account executives, maintaining Salesforce records, meeting pipeline targets, and improving messaging using performance data. The role also collaborates with Sales, Marketing, and Business Operations while developing expertise in identity verification, biometrics, fraud prevention, and digital banking markets.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

Leads executive discovery, consulting opportunity strategy, solution architecture, proposals, SOWs, commercialization, and delivery mobilization for enterprise data, analytics, AI, and GenAI engagements. Advises clients on modern data platforms, cloud modernization, governance, security, ML, and responsible AI. Requires strong presales, delivery leadership, workshop facilitation, architecture, estimation, and commercial skills across Snowflake, Databricks, Microsoft Fabric, AWS, Azure, and Google Cloud.

Analyze malware, binary files, customer detection tickets, and machine learning detections to improve detection efficacy and reduce false positives. Review product detections, assess binary legitimacy, address internal detection questions, and provide insights to data science teams. The role collaborates with technical account managers, incident response teams, data scientists, and malware researchers. Required capabilities include malware analysis, reverse engineering, Python scripting, binary fundamentals, threat assessment, research methods, operating systems, and AI-assisted workflow improvement.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

Develop and scale AI-powered observability features for incident detection, triage, response, and automated analysis. Build LLM- and agent-based workflows, rapidly prototype and validate solutions with users, and collaborate with product, design, and data teams. The role requires production software engineering, cloud-native experience, observability knowledge, and practical GenAI expertise, with opportunities to work on agent frameworks, model fine-tuning, and infrastructure tooling.

Design, build, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly prototype, iterate with real users, integrate AI into alerting, runbooks, and developer workflows, and ensure solutions are scalable, maintainable, and production-ready while collaborating cross-functionally.

Develops data science models, analytical processes, visualizations, dashboards, and reports for U.S. Navy electronic warfare systems. Integrates logistics, engineering, maintenance, reliability, and operational data to evaluate system readiness, reliability, maintainability, performance, risks, and lifecycle sustainment. Applies data mining, statistical analysis, NLP, and machine learning to support operational decisions and collaborates with engineers, logisticians, analysts, and program personnel.

Design and deliver enterprise-grade generative AI solutions on AWS using Bedrock, AgentCore, SageMaker, LLMs, RAG pipelines, vector databases, and agentic workflows. Build scalable APIs and integrations, optimize prompts and models, evaluate performance, and implement security and governance. Collaborate with application, data, and platform teams, troubleshoot production systems, define best practices, and mentor engineers while remaining hands-on.

Executes systems engineering across R&D platforms, hybrid cloud, SaaS, HPC, containers, integrations, data pipelines, observability, and AI architectures. Coordinates Enterprise IT, vendors, cybersecurity, compliance, and scientific stakeholders to deliver secure, scalable, validated, and supportable solutions. Develops technical documentation, deployment plans, runbooks, monitoring, lifecycle artifacts, risk logs, and operational handoffs. Implements RAG, vector database, AI gateway, agentic workflow, and multimodal integration patterns while supporting cloud migration, change management, governance, and technical issue resolution.

Build and operate Lakehouse data pipelines, product features, machine learning and generative AI components, testing, monitoring, instrumentation, and documentation. Collaborate with product managers, engineers, and business users to clarify requirements, deliver production solutions, investigate defects, and improve reliability and performance. Participate in code reviews, technical design discussions, and backlog-driven development while following engineering standards and contributing to scalable Data & AI products.

Leads application engineering teams delivering cloud-native, AI-enabled solutions on Microsoft Azure for Medicare and Medicaid claims-processing operations. Oversees architecture, software development, quality automation, integrations, support, governance, security, vendor relationships, and large-scale implementations. Develops Azure applications as needed, mentors staff, manages cross-functional initiatives, ensures HIPAA compliance, and partners with business and executive stakeholders to improve healthcare payer outcomes.

Drive 100% new-business sales for Conga’s enterprise SaaS solutions across assigned territories. Prospect, develop, and close commercial opportunities; understand client challenges; craft solution-based proposals; manage territory, account, and opportunity plans; and exceed revenue and quota targets. Collaborate with sales engineering, marketing, customer success, professional services, partners, and other teams to deliver value to B2B customers.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Lead biostatistical support for Phase I–IV clinical trials, including study design, sample size calculations, exploratory and efficacy analyses, simulation studies, statistical analysis plans, regulatory submissions, and interpretation of results. Provide strategic guidance to cross-functional teams, oversee vendor-supported work, apply advanced statistical methods, and communicate technical findings clearly to technical and non-technical stakeholders.

Leads enterprise release management operations, governance, audit readiness, cloud and platform modernization, and AI transformation initiatives. Oversees global teams, release execution across cloud and mainframe environments, ServiceNow and ChangeMan capabilities, CI/CD integration, automation, vendor management, compliance, and operational metrics. Partners with Engineering, DevOps, Security, Risk, Compliance, and Audit teams to improve release quality, delivery speed, resilience, and governance while developing team talent and transformation roadmaps.

Leads enterprise release and change management operations, governance, audit readiness, cloud modernization, and AI transformation initiatives. Oversees global teams, vendors, staffing, and strategic roadmaps while improving release quality, compliance, automation, deployment velocity, and operational resilience. Partners with Engineering, DevOps, Security, Risk, Compliance, and Audit teams to implement AI-enabled release intelligence, predictive risk assessment, automated workflows, CI/CD governance, ServiceNow improvements, and Mainframe ChangeMan modernization.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Build and deploy advanced machine learning and agentic AI systems that improve Airbnb’s customer service experience. Responsibilities include developing AI assistants, integrating ML products, optimizing model performance, applying LLM techniques such as fine-tuning and RAG, building multi-agent architectures, and shipping scalable production ML systems. The role collaborates closely with Engineering, Product, and Design teams and contributes to emerging AI initiatives from concept through production.

Develop and deploy machine learning models for fraud detection and credit risk decisioning. Own the full ML lifecycle, including data exploration, feature engineering, experimentation, validation, deployment, monitoring, and optimization. Analyze fraud trends and portfolio outcomes, collaborate with Product, Engineering, and Risk Operations, apply generative AI tools, and mentor fellow scientists while delivering measurable improvements in fraud prevention and responsible credit access.

Own the complete machine learning model lifecycle, from experimentation and validation through deployment, monitoring, retraining, rollback, and retirement. Build reusable training, evaluation, deployment, orchestration, and CI/CD infrastructure. Establish model promotion standards, versioning, lineage, monitoring, alerting, and incident-response processes. Partner with data scientists to preserve scientific validity, improve reliability and efficiency, support delayed-label use cases, mentor teammates, and define MLOps priorities.

Leads cybersecurity architecture for software applications and platforms within wealth management. Drives secure product design, evaluates current and emerging technologies, establishes target architectures, and guides business and technical stakeholders. Oversees alignment with enterprise frameworks, regulatory requirements, scalability, resilience, and software development lifecycle practices. Serves as a subject matter expert in architecture governance and collaborates across teams to deliver secure, high-volume enterprise solutions.

Own the end-to-end computer vision platform for fine-grained item identification and pricing. Responsibilities include designing and fine-tuning vision models, defining evaluation and confidence standards, building labeling feedback loops, prioritizing coverage versus accuracy, and deploying reliable, low-latency serving infrastructure at scale. The role also communicates model capabilities and trade-offs to technical and non-technical stakeholders.

Build and operate distributed GPU training platforms for foundation models. Run and debug multi-node PyTorch and Ray Train workloads, tune GPU networking and scheduling, profile performance, and develop benchmarks and evaluation harnesses. Partner with infrastructure teams and client scientists to improve throughput, reliability, cost efficiency, observability, and end-to-end training pipelines on GKE.