Top Remote Machine Learning Jobs in Orlando, FL

Leads enterprise stress testing and quantitative risk modeling across loan, deposit, capital, and liquidity portfolios. Develops scenario analyses, forecasting models, and proprietary methodologies using statistical modeling, machine learning, Monte Carlo simulation, Python, and SQL. Evaluates model performance, supports governance and validation, provides independent challenge, and communicates portfolio risks and analytical findings to senior management, risk committees, and cross-functional partners.

Own the strategy, roadmap, and lifecycle for audience measurement and media data products across streaming, broadcast, and cable. Partner with engineering, data science, research, analytics, customers, and stakeholders to deliver scalable, accurate, privacy-conscious measurement solutions. Define priorities and success metrics, manage cross-functional initiatives, communicate results to leadership, and mentor product team members.

Provides bioinformatics consultation, computational analysis, statistical support, and production pipeline development for HIV-related genomic and clinical research at NIAID. Responsibilities include sequence and gene expression analysis, variant calling, genome assembly, comparative genomics, proteomics, and epigenomic workflows. The role supports scientific programming, experimental design, data dissemination, scalable research resources, and publications. Candidates need expertise in programming, workflow frameworks, containerization, high-performance computing, and next-generation sequencing, with HIV research experience preferred.

Lead go-to-market strategy for Sprout Social’s AI portfolio, including the Trellis AI agent. Develop enterprise launch playbooks, positioning, executive messaging, campaigns, and enablement programs. Analyze market and competitive dynamics, influence product roadmaps, improve launch processes, and partner with Product, Sales, Revenue, Brand, and executive leadership to drive adoption, revenue growth, deal expansion, and category leadership. The role also mentors marketing team members and operates with substantial autonomy.

Leads technical direction for GitLab’s Core DevOps services, covering CI, CD, planning, and source control. Defines architecture, quality metrics, migration strategies, and technical roadmaps; addresses systemic risk and complex design decisions; prototypes and writes code when needed; partners with Infrastructure, Security, SRE, and Product; adopts AI capabilities; mentors senior engineers; supports incident response and senior technical hiring.

Lead the architecture, development, integration, governance, and deployment of enterprise AI and machine learning solutions. Define technical standards, roadmaps, security and compliance practices, and cloud-native architectures across Azure, AWS, or Google Cloud. Guide engineering and data science teams, conduct architecture and code reviews, evaluate emerging technologies, mitigate technical risks, support proposals, and communicate solutions to government stakeholders and organizational leadership.

Own customer deployments of Sayari’s risk screening, graph analytics, investigation workflows, and data integrations. Responsibilities include technical scoping, production Python and SQL development, API integration, bulk data delivery, pipeline onboarding, dashboard creation, monitoring, troubleshooting, documentation, and feeding recurring customer needs back to engineering. The role requires client-facing communication, independent problem-solving, and collaboration across multiple customer accounts, with 25–30% travel and in-office time.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Designs and maintains automated, AI-enabled detection and response capabilities across enterprise security platforms. Integrates SIEM, EDR, SOAR, and threat intelligence technologies; analyzes security telemetry; develops and validates detection content and automation workflows; documents procedures and runbooks; and collaborates with SOC, threat intelligence, and incident response teams. The role also evaluates emerging security capabilities and supports continuous improvement of detection operations for a federal agency.

Paid, part-time Creative Advisor collaborating with AI research and machine learning teams. The role involves sharing creative expertise, defining quality standards, developing work, reviewing model outputs, identifying limitations, and helping fellow creators engage with the program. Applicants should be leading creators with strong portfolios and interest in shaping AI’s impact on creative practice; no AI experience is required.

Applies statistics, experimentation, data analysis, and machine learning to complex datasets and AI model evaluation. Designs analyses, develops and validates models, performs Python and Pandas data transformations, creates expert prompts and datasets, identifies quantitative errors, and provides structured feedback. Produces reproducible code, analytical reports, and clear documentation while collaborating with stakeholders and reviewing peer contributions.

Defines enterprise technology architecture, modernization roadmaps, governance standards, and cloud, infrastructure, networking, security, resilience, and AI strategies. Designs Azure, AWS, and hybrid environments; guides migrations, automation, identity, connectivity, disaster recovery, and cost optimization. Leads architecture reviews, evaluates vendors and emerging technologies, translates regulatory requirements into controls, and influences executives and cross-functional technical teams through implementation.

Owns the strategy, roadmap, development, and adoption of financial data products and APIs. Partners with data science and engineering to integrate predictive and machine-learning models into scalable, trustworthy capabilities. Defines quality metrics, requirements, documentation, and support models while ensuring privacy, security, regulatory compliance, and downstream adoption across consumer and business banking products.

Leads enterprise AI platform strategy, architecture, engineering, governance, and operations across machine learning, generative AI, agents, data, and business intelligence. Owns platform standards, reusable services, lifecycle management, security, compliance, reliability, vendor commitments, budgets, and consumption. Directs production-scale AI enablement, establishes responsible AI controls, and manages engineering teams, staffing, operating plans, and executive stakeholder alignment.

Own strategy and execution for integrated B2B marketing campaigns that drive enterprise pipeline, self-service growth, platform adoption, and category awareness. Coordinate ABM, content, paid media, events, lifecycle, social, partnerships, PR, and sales activation. Manage launches, optimize audiences and conversion paths, orchestrate teams and agencies, and measure impact across engagement, pipeline, acquisition, product adoption, and awareness.

Own ElevenLabs’ North American enterprise financial services sales motion across banking, insurance, lending, payments, and wealth. Define the ideal customer profile, sales methodology, pipeline and forecasting processes, land major enterprise accounts, and expand deployments. Build and lead the sales team, guide regulated buyers through risk, compliance, model-risk, architecture, and security reviews, and manage executive negotiations. Partner with engineering, product, marketing, and customer success to develop repeatable conversational AI sales strategies.

Combines wastewater process engineering with AI, machine learning, process modeling, digital twins, and optimization. Responsibilities include analyzing treatment data, developing biological and chemical process models, evaluating and troubleshooting facilities, conducting experiments, testing technologies, and implementing predictive or optimization solutions. The engineer collaborates with multidisciplinary project teams and utility clients to improve treatment performance, reliability, energy use, and operational decision-making. Python programming and wastewater modeling platforms are preferred, along with wastewater treatment, nutrient removal, data science, and technical communication expertise.

Drafts, reviews, and negotiates complex data, content, licensing, distribution, vendor, and supplier agreements. Advises internal teams on intellectual property, data and content rights, AI and machine learning use cases, privacy, regulatory compliance, and commercial disputes. Partners with Content, Sales, Marketing, Finance, and Product teams; develops policies, playbooks, and project plans; monitors evolving AI and data regulations; and provides broader corporate legal support.

Provides advanced technical support for robotic systems during commissioning and 24/7 customer operations. Diagnoses hardware, software, controls, Linux, network, and automation failures; analyzes logs and telemetry; develops integrations and support workflows; leads major incidents; coordinates engineering, field service, and customer teams; manages cases and corrective actions; validates AI-assisted diagnostics; and mentors engineers while improving system reliability and recovery times.

Provides statistical leadership for Medical Affairs, real-world evidence, comparative effectiveness, HTA, and HEOR activities. Designs and analyzes observational, clinical, Phase IV, and indirect treatment comparison studies; evaluates data quality, bias, uncertainty, and applicability; supports evidence-generation strategies, reimbursement submissions, publications, and scientific communications. Collaborates with statistics, epidemiology, RWE, medical, and external scientific teams to translate complex analyses into actionable conclusions.

Performs data science projects involving statistical modeling, machine learning, forecasting, data cleaning, exploratory analysis, and AI-assisted workflows. Builds and maintains models, pipelines, reporting, and analytics-ready datasets using Python, SQL, Snowflake, and dbt. Supports validation, documentation, testing, governance, and stakeholder communication while developing proficiency in modern data science tools and deployment practices.

The Senior Business Development Representative will generate enterprise sales pipeline through account research, lead identification, outbound calls, emails, and social outreach. The role qualifies opportunities, maintains accurate Salesforce records, represents Rasa’s AI platform, collaborates with sales and marketing teams, and works toward exceeding quotas. This fully remote position requires strong communication, organization, customer empathy, autonomy, and comfort working in a fast-paced startup environment.

Own the strategy and roadmap for Constructor’s search quality and Product Discovery Engine. Drive measurable improvements in relevance, conversion, revenue per visit, and GMV across retail verticals. Partner with ML, data science, and engineering teams to productionize state-of-the-art retrieval, ranking, query understanding, personalization, and LLM capabilities. Establish evaluation frameworks, investigate quality issues, align multiple teams, and expand into chapter-wide product leadership.

Lead end-to-end delivery of strategic data science and AI solutions for AI infrastructure. Work with telemetry and experimentation at fleet scale to build, deploy, and measure predictive and prescriptive models. Partner with stakeholders, present insights, enable adoption, and promote responsible AI. Collaborate closely with data engineers and infrastructure teams to productionize scalable ML solutions.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.