Top Machine Learning Jobs in Orlando, FL

Owns and grows strategic Financial Services accounts by prospecting, developing channel and alliance opportunities, presenting cybersecurity solutions, closing new business, and expanding customer relationships. The role requires enterprise technology and security sales expertise, executive engagement, account planning, forecasting, reseller collaboration, and the ability to explain NDR, Agentic SOC, AI security, and network visibility solutions. Travel is primarily remote and on the road, with at least 50% travel required.

Leads technical support for VA healthcare AI citizen developers, developing and validating supervised, unsupervised, and reinforcement learning models. Builds synthetic healthcare data cohorts, designs secure cloud-based data science workflows, collaborates with federal stakeholders, interprets analytical results, mentors developers, and ensures model quality, documentation, privacy, governance, and regulatory compliance.

Build and scale production AI systems for Prophetic’s real estate intelligence platform. Responsibilities include developing agentic systems, data pipelines, inference workflows, evaluation benchmarks, geospatial processing, and machine learning applications across hundreds of millions of records. The role requires shipping reliable Python services, optimizing throughput and cost, integrating research into products, and collaborating with engineering and product teams.

Leads Qvest’s Parks & Resorts consulting practice by overseeing complex technology implementations, advising executive clients, managing delivery risks, budgets, timelines, and teams, and driving business development and revenue growth. Develops strategic market plans, expands client relationships, represents Qvest at industry events, and delivers digital transformation, machine learning, and applied AI solutions. Requires deep Parks & Resorts expertise, strong consulting leadership, executive communication, and regular travel to Orlando and client sites.

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.

Leads the hands-on design, development, and delivery of applied AI and machine learning products for software engineering workflows. Directs technical discovery, prototyping, model evaluation, architecture, MLOps/LLMOps, and production deployment while managing and mentoring an engineering team. Partners with engineering, product, and business stakeholders to define scalable, secure, responsible AI solutions and measure developer and business impact.

Supports federal, state, local, tribal, and territorial emergency management planning through geospatial and data analytics. Develops emergency operations, hazard mitigation, and continuity plans; applies FEMA planning processes; facilitates stakeholder coordination; supports exercises, disaster activations, and deployments. Performs quantitative and qualitative analysis, develops database and modeling solutions, applies AI and machine learning techniques, and ensures compliance with government regulations. This is an on-call, non-full-time role requiring frequent travel and extended deployments.

Lead design, build, and deploy scalable, reliable Java-based microservices in a cloud-native, event-driven AWS environment. Own backend feature design, code quality, automated testing, and FedRAMP release processes. Mentor engineers, participate in on-call/TechOps, integrate AI tools into development, and drive architecture, observability, and cross-team initiatives.

As a Customer Success Engineer, you'll drive adoption and satisfaction for enterprise customers using Deepgram's voice AI technology, fostering strong relationships and aligning technical solutions with business objectives.

Leads the architecture, development, and operation of Disney’s AI and machine learning platform for finance. Responsibilities include managing a global engineering team, building production LLM applications and governed data foundations, defining AI/ML roadmaps, enforcing RBAC and engineering quality, guiding build-versus-integrate decisions, partnering with finance stakeholders, and driving enterprise adoption. The role is hands-on, involving architecture, implementation, technical reviews, code review, model evaluation, and delivery oversight.

Own product areas for an autonomous banking platform, translating backend capabilities (APIs, async workflows, ML) into user-facing features. Conduct customer discovery, define technical requirements and acceptance criteria, prioritize backlog, partner with engineering on scoping and de-risking, and validate outcomes using feedback loops and analytics.

Lead the Product and Design organization, define product vision and roadmaps, prioritize strategic investments, and align cross-functional teams to deliver safe, scalable robotic delivery experiences. Build and develop high-performing product and design teams, champion user-centered design, guide data-driven decisions, and translate complex robotics, hardware, software, and operational capabilities into customer value. Partner with executive leadership across Engineering, Operations, Safety, Policy, and Business Development to drive commercialization and growth.

Lead performance marketing data science initiatives focused on lead segmentation, bidding optimization, targeting, and marketing spend allocation. Develop and deploy production-ready machine learning systems, establish research and ML operations roadmaps, build monitoring frameworks, and identify high-impact opportunities. Partner with engineering, marketing, vendors, and executives while mentoring data scientists and machine learning engineers and promoting rigorous, maintainable, and reproducible practices.

Build and deploy AI agents, intelligent applications, scalable backend services, APIs, and full-stack solutions. Integrate large language models, AI services, data platforms, and external APIs into customer-focused products. Collaborate with engineering, product, and cross-functional teams while contributing to cloud-native applications, data pipelines, distributed systems, testing, deployment, and production operations.

Leads AI presales strategy, customer advisory, technical solution architecture, and team development. Translates business needs into AI use cases, prototypes, business cases, roadmaps, and repeatable offerings. Partners with sales, engineering, consultants, and technology vendors to support complex pursuits, executive briefings, demonstrations, proofs of concept, and AI business growth. Promotes responsible AI governance, tracks pipeline performance, develops thought leadership, and remains hands-on in high-impact customer engagements.

Lead and manage the underwriting and credit risk model validation team, perform hands-on independent validations of complex ML models, assess data integrity and performance, identify and remediate model weaknesses, partner on MRM framework improvements, and coordinate with audit, compliance, and controls to meet regulatory standards.

Drive new enterprise revenue and customer expansion by selling AI-powered SaaS solutions. Manage complex 6–12 month sales cycles, build executive relationships, develop value propositions, maintain pipeline accuracy, lead virtual account teams, and close high-value deals. Translate technical AI capabilities into measurable business outcomes, guide customer adoption, collaborate with marketing, product, and customer success teams, and exceed quarterly pipeline and revenue targets.

Lead product vision, strategy, discovery, and Agile delivery for insurance platforms. Partner with engineering, data science, design, QA, and insurance domain teams to prioritize opportunities, manage backlogs, evaluate build-versus-buy decisions, guide technical design, coordinate delivery, and measure outcomes. The role requires strong knowledge of insurance policy lifecycles, integrations, data modeling, pricing, underwriting, and AI-enabled workflows.

Leads product strategy, roadmap prioritization, AI and automation initiatives, client engagement, and cross-functional delivery for SPINS Intelligence and Portal. The role combines hands-on product management with PM team leadership, including writing user stories, defining acceptance criteria, triaging issues, reviewing sprint output, managing product economics, and developing business cases. Requires strong CPG, SaaS analytics, data product, AI/ML, and technical platform expertise.

Own Sophos EDR strategy, roadmap, technical product development, AI-powered experiences, and go-to-market execution. Translate customer needs, threat research, telemetry, competitive intelligence, and business goals into product priorities. Partner with engineering, security operations, threat research, marketing, sales, and customer success to deliver endpoint detection, investigation, threat hunting, response, reporting, and XDR/MDR upgrade capabilities while driving adoption, revenue, and customer outcomes.

Architect and prototype scalable solutions for Twilio’s emerging technologies experimentation team. Lead AI, machine learning, and LLM-driven initiatives; guide technical direction; mentor engineers across the US and India; align prototypes with business goals; and ensure enterprise architecture, scalability, extensibility, and production readiness. The role requires extensive architecture or principal engineering experience, cloud expertise across AWS and Azure, full-stack development knowledge, database expertise, and strong technical leadership.

Run Discovered Labs’ founder-led content operation across LinkedIn and YouTube, from topic selection and editorial development through publication and performance analysis. Responsibilities include shaping founder content, coordinating external partners, repurposing long-form material, improving AI-supported content systems, and using engagement and pipeline data to guide editorial decisions. The role requires strong editorial judgment, hands-on execution, social content expertise, and the ability to work autonomously in an AI-forward, remote environment.

Annotate and label data accurately according to project guidelines, conduct quality checks, provide feedback on annotation tools and processes, collaborate with project teams, maintain efficiency, and identify issues for resolution. The role requires strong attention to detail, critical thinking, adaptability, communication skills, and comfort with repetitive computer-based work.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Own the AI product strategy and roadmap for HealthEdge’s HealthRules Payer platform. Identify and prioritize AI use cases across claims, member management, enrollment, benefits, and contracts; translate strategy into product requirements; and align product, engineering, data science, UX, marketing, and sales teams. Lead AI governance, regulatory compliance, customer presentations, go-to-market messaging, pilots, and feature commercialization while ensuring responsible AI use in healthcare payer workflows.