Top Remote Machine Learning Jobs in Tampa, FL

Lead the SDL and Cash Line ML teams, developing innovative machine learning models to improve lending solutions and expand credit access. Collaborate with cross-functional teams to drive product strategy and enhance model performance, while mentoring a team of ML scientists.

As a Data Scientist, you will develop algorithms, collaborate on product requirements, and communicate data-driven insights, while enhancing product offerings and delivering actionable insights.

Manage a team of data scientists to develop fraud detection models, lead planning and collaboration with leadership, and communicate outcomes effectively while maintaining strong business intuition and technical leadership.

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

The Senior Product Manager will oversee the strategy and development of telematics data products, manage cross-functional teams, and ensure product viability and market alignment through customer insights and technical fluency.

The Senior Staff Machine Learning Scientist will lead research initiatives in computer vision and multimodal understanding, develop novel models, and translate research into practical applications while mentoring team members.

Lead the Data + ML Platform team to design, build, and operate scalable data and MLOps infrastructure. Drive platform strategy, enable ML experimentation-to-production workflows (model registry, feature stores, drift monitoring, experiment tracking), modernize streaming and batch pipelines, and scale the team while partnering cross-functionally to deliver measurable outcomes.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Develop ML/RL models and infrastructure for AV simulations, optimize training loops, and support project timelines in autonomous vehicle tech.

Hands-on role building and maintaining an Azure cloud data platform and Lakehouse medallion architecture. Implement ELT/ETL pipelines with Databricks, integrate ERP and other sources, enforce Unity Catalog access policies and RBAC/ABAC, monitor pipeline health, troubleshoot incidents, and produce technical documentation and runbooks. Support modernization, governance, and team best practices.

Lead enterprise application strategy and architecture for Finance and G&A, drive high-velocity billing consolidation, embed AI/automation across core workflows, ensure SOX compliance and strong controls, and build/scale an applications team while partnering with execs and vendors to accelerate transformation.

The Enterprise Account Executive will drive revenue growth by managing complex sales cycles, closing deals with Fortune 500 companies, and understanding both technical and business needs.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

The VP of Product Development will lead a global engineering team for eDiscovery platforms, focusing on modernization, reliability, and AI integration while managing execution and stakeholder communication.

Develop and experimentally validate novel ML architectures to improve prediction and design of in‑vivo molecular interactions, extending structure‑informed generative models to causal models and integrating with deployed drug‑design workflows.

Lead technical solution development for military and Federal civilian health capture and proposal efforts, ensuring technical compliance and producing quality architecture designs.

The Customer Success Engineer will lead deployments, serve as a technical advisor, and support customer engagement, focusing on product enhancements and user experience.

Support AI training initiatives by developing and refining financial models, translating market research and due diligence into structured AI training datasets, and partnering with stakeholders to convert complex financial concepts into training inputs. Evaluate and improve AI-generated financial analyses, identify gaps in modeling or valuation assumptions, and contribute strategic insights to enhance AI-powered private equity tools in a fully remote environment.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Create and curate high-quality graphic design datasets and training materials for AI systems. Collaborate with engineers to evaluate and refine AI-generated visuals, document design processes and style guides, and translate design concepts into actionable training data. Stay current with design trends and communicate feedback to cross-functional teams.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

Remote neurologist AI trainer who annotates and reviews clinical data, guides ML model training, interprets neurological diagnostics (EEG, EMG, LP), and communicates clinical insights to interdisciplinary teams to improve AI-driven diagnostic and treatment tools.