Top Machine Learning Jobs in Tampa, FL

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

Lead end-to-end ML system development: data exploration, model research, training, deployment, evaluation, and MLOps. Collaborate cross-functionally to prioritize claims, improve matching, measure business impact, and own a technical workstream.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Build and maintain Quora's ML platform and ranking infrastructure to serve large-scale ML models. Work on GPU model serving, distributed systems, developer tooling, feature store modernization, and on-call rotation to ensure high availability, performance, and cost efficiency.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

Lead development of internal Python SDKs and agentic AI workflows, build scalable FastAPI ML services, maintain CI/CD with Jenkins/GitLab Runners, and use Galileo for model evaluation and observability to productionize autonomous systems.

Join an R&D team to develop image-processing and computer-vision algorithms for 2D/3D scene segmentation, design experiments to detect long-tail model failures, and explore deep learning models and active learning methods to improve autonomous robotic vision systems.

Lead end-to-end development of fraud and risk ML systems: define problems, engineer features, build and evaluate models, collaborate with product and engineering, deploy with ML engineers, and monitor production performance to detect drift and trigger retraining.

Lead AI/ML consulting engagements: design and implement ETL, data modeling, predictive models, anomaly detection, distributed data pipelines, and executive-ready visualizations. Drive business development, mentor teams, and deliver client-facing solutions in hybrid/onsite environments.

Lead AI/ML consulting engagements, build and deploy machine learning models and ETL pipelines, analyze large datasets, design data architectures, deliver visualizations, drive business development, and manage client-facing teams in high-stakes situations.

Lead discovery and design of end-to-end enterprise AI/ML architectures, define MLOps/LLMOps and data lifecycles, integrate solutions with enterprise systems and security, produce reference patterns and runbooks, mentor teams, and scale repeatable delivery through accelerators while partnering with clients and risk/security stakeholders.

Design, build, evaluate, and deploy predictive ML models across regression, classification, NLP, computer vision, and time-series problems. Perform data cleaning, feature engineering, EDA, model selection, tuning, explanation (SHAP, feature importance), and collaborate with engineers and stakeholders to operationalize and monitor models in production.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

Lead architecture and technical strategy for large Azure programs, define modernization blueprints and reusable reference architectures, drive enterprise AI/ML adoption, mentor architects, ensure cross-team alignment on security, performance, and observability, and support sales and practice growth through solutions, playbooks, and reusable assets.

The Staff Engineer will design and manage ML infrastructure while mentoring teams, driving MLOps strategies, and ensuring compliance in healthcare data systems.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.

Design and develop machine learning systems for construction robots, including scalable infrastructure, computer vision models, and data analysis for performance improvement.

Build and design backend services, AI agents, and machine learning models. Collaborate cross-functionally to deliver AI features and maintain code quality.

As a Senior Machine Learning Engineer at ZoomInfo, you will enhance core datasets through ML/NLP solutions, manage entity resolution tasks, and drive cross-functional impact by mentoring teams and collaborating on ML KPIs.

Lead and scale Vibrant's data science, ML, and AI function: set strategy and governance, oversee applied research and production ML (especially NLP), ensure HIPAA-compliant deployments, partner with execs and engineering, recruit and mentor teams, and deliver measurable outcomes for crisis services and enterprise initiatives.

Build and maintain backend services and infrastructure for ML and generative AI features. Develop scalable, resilient, high-performance Python services (async frameworks, streaming), deploy to Kubernetes, integrate RAG and LLM orchestration, instrument LLM observability, and collaborate cross-functionally to deliver production-ready AI products.

Build and maintain scalable ML and generative AI platforms and backend infrastructure; deploy and manage production applications; create services exposing ML/AI products; implement RAG solutions and vector databases; integrate LLM orchestration and foundation model APIs; collaborate cross-functionally and mentor teammates.

Support climate and Earth system researchers by developing, deploying, and optimizing AI/ML workflows on HPC and GPU-accelerated systems. Assist with compiling, profiling, tuning, and porting scientific applications; optimize system utilization (CPU/GPU, memory, storage, I/O); maintain scientific software environments and datasets; provide consulting, training, documentation, and grant support to advance AI-enabled climate research.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.