Top Remote Entry Level Jobs in Tampa Bay, FL

Lead end-to-end development of an AI-driven decision-support system for multi-sensor data. Responsibilities include building data ingestion and fusion pipelines, training and deploying supervised and unsupervised ML models, optimizing near-real-time inference, creating GPU-accelerated dashboards, integrating modular containerized systems, and supporting secure deployment and testing. The role works directly with the founder, owns technical delivery autonomously, and collaborates with subcontractors on human factors, model validation, and security auditing.

Create clear, concise UX copy across patient-facing and business healthcare workflows, including onboarding, forms, errors, notifications, AI interactions, and transactional communications. Partner with Design, Product, Engineering, Research, and users to improve information architecture, terminology, interaction patterns, and content systems. Develop voice and tone guidelines, reusable content patterns, and scalable AI-assisted workflows while ensuring clarity, accuracy, accessibility, consistency, and trust in complex healthcare software experiences.

Sell Azenta’s sample repository, biorepository, laboratory processing, storage, cryo-storage, transportation, and sample management services to government, academic, healthcare, and research customers. Responsibilities include developing accounts, generating leads, responding to public-sector opportunities, preparing proposals and quotations, delivering capability presentations, maintaining Salesforce records, coordinating internal stakeholders, and reporting pipeline and forecasts. The role requires approximately 50%–60% overnight travel within the United States.

Resolves Texas title curative issues and manages post-closing processes, including title research, lien and judgment clearance, document preparation, recording coordination, policy issuance, file tracking, and compliance with underwriter and Texas title insurance requirements. Coordinates with escrow officers, lenders, attorneys, underwriters, and external parties while maintaining accurate, audit-ready files.

Research and optimize AI and machine-learning systems across desktop and mobile platforms. Evaluate model performance, develop and deploy on-device applications and agents, and improve browser ML products. Apply systems and network expertise, collaborate with privacy and security researchers, and investigate AI risks. The role involves open-source development, performance benchmarking, and use of modern ML frameworks to deliver privacy-focused solutions.

The job description provides no specific responsibilities, qualifications, technologies, compensation, travel requirements, or clearance requirements for the Lexis Associate position.

Prospects and qualifies leads, develops client relationships, delivers sales presentations, negotiates and closes deals, meets sales targets, maintains CRM records, researches markets and competitors, and collaborates with marketing, account management, and operations teams.

Own the full sales cycle for inbound and self-generated opportunities, from discovery and e-commerce business diagnosis through proposals, objection handling, signatures, and payment collection. Sell monthly agency retainers to e-commerce owners, maintain accurate CRM records, follow up persistently, and coordinate with delivery teams. Success is measured primarily by cash collected, along with close rate, deal size, speed to close, and pipeline hygiene.

Contact prospects through email, calls, and LinkedIn to qualify leads and book meetings for account executives and field engineers. Research prospects using Sales Navigator and ZoomInfo, manage outreach sequences, track activities in Salesforce and Outreach, respond to chat inquiries through Drift, meet activity and booking targets, and follow up on marketing-generated leads. The role is remote in North America and may involve travel to in-person events.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.