Top Remote Machine Learning Jobs in Orlando, FL

Build and own production Vision AI systems end to end, including model selection, inference pipelines, evaluation, monitoring, and customer-facing features. Shape architecture, collaborate with product, design, and engineering teams, and use AI coding tools to accelerate development. Establish high standards for quality, testing, reliability, security, and privacy while mentoring engineers and taking ambiguous ideas from prototype to production.

Lead AI and autonomous vehicle behavior safety strategy for Level 3–4 automated driving systems. Own behavior safety claims, evidence criteria, validation methodologies, performance indicators, and launch-readiness assurance. Evaluate ML-driven AV performance using simulation, closed-course, and public-road data. Define safety metrics and strategies for imitation and reinforcement learning systems, interpret applicable standards, and influence cross-functional engineering teams and executives.

Provides principal-level ServiceNow consulting, architecture, engineering, and delivery oversight for enterprise clients. Leads technical solution design, implementations, automation, cloud deployment, presales estimation, workshops, and client advisory activities. Establishes technical standards, mentors engineers and architects, supports governance and practice growth, and serves as an escalation point. The role also develops AI-enabled ServiceNow offerings and requires approximately 10% travel.

Build and tune fraud detections across accounts, authentication, payments, and other high-risk flows. Lead threat hunts, identify emerging abuse patterns, improve fraud telemetry, and convert findings into automated, tested detections. Develop tools, dashboards, and enrichment for fraud operations; measure precision, recall, coverage, and detection speed; and collaborate with security, engineering, risk, product, and operations teams to address systemic fraud risks.

Develop and lead complex AI and data science solutions, shape AI technical strategy, research emerging technologies, resolve large-scale technical problems, and guide cross-functional teams. The role focuses on scalable data solutions, machine learning, deep learning, large language models, generative AI, NLP, computer vision, Python, and Vertex AI. It requires a bachelor's degree and at least five years of progressive data analytics and data science experience.

Lead teams building Samsara Assistant, Agent Studio, and agent extensibility. Own product growth, activation, answer quality, trust, repeat usage, and proactive agent capabilities. Set technical direction for an AI platform used by customers and internal product teams, partner across Safety and Maintenance, and lead approximately 10 machine learning, applied science, and full-stack engineers through two tech leads. Hire, develop, and manage a high-performing engineering organization.

Lead and transform the Fraud Intelligence Team into a high-impact function by improving operating models, QA, tooling, and metrics; build entry-level talent programs; connect labeling to measurable fraud prevention outcomes; deliver actionable insights to Product, R&D, Analytics, and GTM; and leverage AI and automation to increase productivity and business impact.

Lead product strategy, roadmap, and execution for Zscaler’s threat detection and response offerings within its Agentic SOC and MDR business. Translate threat intelligence and customer needs into innovative security products, coordinate engineering and go-to-market teams, and measure product impact. The role owns commercialization, technical direction, product quality, and business outcomes while requiring deep cybersecurity expertise and experience with enterprise security products.

Lead Amazon performance marketing strategy and execution, manage Sponsored Ads and DSP, collaborate with agencies and cross-functional teams, optimize campaigns and budgets, analyze performance with AMC/analytics, and measure commercial impact to drive ROAS and customer acquisition.

Leads strategy, roadmap, investment, and execution for horizontal AI capabilities, shared AI platforms, and AI-native cybersecurity products. Translates AI research into reliable, secure, scalable customer solutions; drives adoption across product teams; establishes launch and autonomy criteria; partners with customers and cross-functional leaders on commercialization; and builds a high-performing AI product organization.

Leads complex, cross-functional technical programs for the Superintelligence Lab. Responsibilities include roadmap execution, milestone and dependency management, risk mitigation, issue resolution, stakeholder alignment, leadership reporting, and implementing program-management best practices. The role requires collaboration with engineering, data science, and leadership teams while using AI technologies and Jira to improve workflows, decision-making, and business outcomes.

Lead firmware and embedded software development for AI-powered video safety products. Architect scalable embedded systems, integrate camera and real-time edge computing capabilities, deploy CV/ML models, and guide technical decisions. Collaborate with hardware, cloud, machine learning, and product teams on next-generation devices and complex multi-team projects. Maintain production systems, improve testing and release infrastructure, mentor engineers, and support products from development through mass production.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Own the full B2B sales cycle across EMEA and the US, from outbound prospecting and opportunity creation through negotiation, closing, and account expansion. Sell content syndication, qualified leads, and intent-led demand-generation programs to senior marketing and sales decision-makers. Build pipeline, meet revenue targets, maintain accurate CRM forecasts, grow existing accounts, and collaborate with delivery, data, media, and client success teams.

Own the full sales cycle for enterprise B2B demand-generation clients across EMEA and the US. Responsibilities include prospecting, building outbound pipelines, developing account strategies, engaging executive buyers, negotiating complex contracts, closing net-new business, expanding accounts, forecasting accurately, and collaborating with delivery, data, media, marketing, and client success teams.

Leads a multidisciplinary data science and data engineering team supporting Finance. Sets strategy, prioritizes initiatives, guides analytical models and data architecture, advances AI and machine learning applications, improves data quality and reliability, establishes scalable operating processes, and communicates technical recommendations to Finance and executive stakeholders. The role also manages talent, oversees forecasting and decision-support outcomes, and drives measurable business impact.

Leads GitLab’s Product, Sales, and Finance Data Science teams, managing managers and a 12-person organization. Sets data science standards, prioritizes investments, partners with executives, drives product adoption and revenue growth, and oversees experimentation, causal inference, predictive modeling, and AI initiatives. Develops talent, builds succession, and collaborates with Analytics Engineering and Data Engineering on a shared data and AI roadmap.

Leads AI and automation product strategy for disability and absence claims operations. Responsibilities include prioritizing AI opportunities, owning roadmaps, designing human-in-the-loop workflows, defining safeguards and success metrics, partnering with engineering, coaching Product Owners, managing delivery increments, and communicating operational impact to senior leadership.

Owns strategic planning, governance, prioritization, risk management, and execution across a portfolio of Superintelligence Lab programs. The role establishes phase-gate standards, manages change, tracks portfolio health through dashboards and metrics, resolves blockers, advises cross-functional teams, and communicates progress to executives. It requires strong technical program management experience, Jira proficiency, Agile and SDLC knowledge, and the ability to apply AI technologies to improve business outcomes.

Own and grow strategic enterprise customer relationships, driving retention, renewals, expansion, and seven-figure software transactions. Develop multi-year account plans, build executive and technical stakeholder relationships, identify expansion opportunities, lead negotiations, maintain forecasts and pipeline visibility, and coordinate cross-functional teams. Engage with data scientists and AI/ML engineers to understand adoption, customer outcomes, and opportunities to expand platform usage across business units and use cases.

Own the strategic vision and roadmap for Coursera’s AI-driven search experience. Lead conversational search, personalization, semantic and multilingual search, ranking, and discovery improvements. Partner with engineering, design, research, data science, ML, marketing, and partnerships teams to deliver scalable platform capabilities. Use experimentation, market analysis, KPIs, and customer insights to improve conversion, merchandising, learner experience, and revenue.

Leads GitLab’s Product Data Science team, setting its strategy, roadmap, analytical standards, and priorities. Partners with Product and Engineering leaders on experimentation, causal inference, analytics, and production modeling. Manages, hires, coaches, and retains data scientists while building a high-performing function. Provides hands-on technical review of models, analyses, and queries, and communicates insights and risks to senior leaders.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Leads SailPoint’s global corporate communications function, including corporate narrative, media relations, PR campaigns, crisis communications, executive coaching, podcast strategy, partner communications, and measurement. The role manages internal and agency teams, translates complex cybersecurity and AI concepts into compelling stories, supports product launches and company milestones, builds rapid-response capabilities, and advises executives on reputation and communications strategy.

Own Secret Detection and Vulnerability Research as a product: define strategy across prevention, detection, validation, revocation, and reporting; ship detection content and intelligence with measurable quality; drive adoption, retention, and revenue; partner with engineering, research, and Field; use AI to accelerate research and productization.