Top Remote Machine Learning Jobs in Palm Bay, FL

Annotates, labels, categorizes, validates, and quality-checks structured and unstructured data for AI model training. Maintains dataset accuracy, consistency, documentation, and version control while collaborating with AI engineers, data scientists, and quality teams. The role also supports annotation workflow management, guideline development, calibration exercises, error identification, and continuous process improvement.

Own the strategy, roadmap, development, delivery, and continuous improvement of AI-powered products. Translate business and customer needs into product requirements, coordinate engineering and AI teams, define success metrics, monitor adoption and ROI, and support responsible AI governance. The role also involves stakeholder management, leadership communication, market and technology evaluation, release planning, and ensuring successful product implementation.

Supports AI governance frameworks, policies, risk assessments, compliance reviews, monitoring, reporting, audits, and responsible AI practices. Partners with legal, compliance, security, AI, and business stakeholders to interpret regulations, establish controls, mitigate risks, and improve governance maturity. Tracks regulatory developments, maintains documentation, advises project teams, and promotes responsible AI adoption across the organization.

Assesses and mitigates security risks across AI, machine learning, and Generative AI systems. Responsibilities include threat modeling, vulnerability assessment, security monitoring, incident response, governance, compliance, access controls, policy development, audit support, and advising engineering and business teams on secure AI development and responsible adoption.

Leads operational oversight, performance analysis, governance, and continuous improvement for AI-powered products and services. Develops performance frameworks, executive dashboards, business insights, risk and compliance reporting, and scalable AI operating models. Partners with AI engineers, product teams, and business leaders to optimize workflows, support automation, manage operational risks, and drive cross-functional operational excellence.

Leads the strategy, roadmap, development, launch, and optimization of AI-powered products. Defines product vision, requirements, priorities, success metrics, and adoption goals while coordinating engineering, AI, business, executive, and customer stakeholders. Evaluates emerging AI capabilities, supports responsible AI governance, manages product performance, and drives continuous improvement aligned with business objectives.

Leads AI data annotation operations, managing workflows, resources, production targets, quality standards, audits, and performance metrics. Oversees annotation teams through coaching, recruitment support, onboarding, and performance management. Partners with AI engineers, trainers, product teams, and operational stakeholders to translate requirements into plans. Drives process improvement, automation, tool adoption, data integrity, and operational excellence across AI and machine learning data preparation initiatives.

Identifies business opportunities for AI and automation, gathers and documents requirements, develops business cases, supports solution design and delivery, facilitates stakeholder collaboration, assists with testing and change management, and tracks project outcomes, reporting, and benefits realization.

Owns AI product backlogs, priorities, requirements, user stories, and acceptance criteria. Translates business needs into technical solutions, coordinates stakeholders and delivery teams, supports Agile ceremonies and product releases, monitors adoption and performance, and drives continuous improvement. The role also identifies AI-driven business opportunities, supports responsible AI practices, and contributes to AI product strategy.

Tests and validates AI-powered products, machine learning models, and Generative AI applications. Designs test strategies, executes functional, regression, and user acceptance testing, validates model outputs, identifies defects, analyzes quality metrics, documents evidence, and supports compliance and release readiness. Collaborates with AI engineers, product managers, and business teams to improve testing methodologies, quality frameworks, and AI evaluation practices.

Designs, implements, optimizes, and administers generative and agentic AI solutions. Translates business requirements into AI workflows, oversees model deployment and performance, analyzes implementation data using Power BI, and coordinates responsible AI governance. Collaborates with engineering, security, data science, sales, and reporting teams while supporting users and maintaining AI platforms, cloud services, and low-code tools.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Owns intake and triage for marketing technology requests, learns marketing tools and data flows, and designs automation workflows with engineers and data scientists. Builds solutions using internal frameworks and low-code tools, tests variable AI outputs, guides user acceptance testing, documents processes, enables marketing teams, and measures business impact. The role connects marketing needs with agentic workflows, integrations, custom applications, and ML-based analysis.

Own the technical systems that convert market signals into revenue pipeline. Build and maintain prediction models, multichannel campaign infrastructure, AI-driven personalization, call intelligence, and signal-to-task workflows. The role requires production coding, applied machine learning, API integrations, SQL, CRM and GTM stack expertise, and practical LLM usage. You will manage live revenue systems, improve attribution and data quality, and balance rapid experimentation with reliability.

Analyst supporting a marketing engineering team by translating marketing needs into automated workflows, integrations, internal applications, and AI-enabled solutions. Responsibilities include intake and prioritization, process analysis, workflow design, low-code development, collaboration with engineers and data scientists, testing variable AI outputs, user acceptance testing, documentation, enablement, and impact measurement.

Develop and maintain quantitative models for forecasting, pricing optimization, and risk assessment. Analyze mortgage business metrics to identify revenue, profitability, and operational improvement opportunities. Build performance measurement frameworks, dashboards, automated reports, and SQL-based analytics workflows. Use Python for statistical analysis, automation, and machine learning workflows. Validate and monitor model performance, recalibrating models as needed, while communicating actionable recommendations to business leaders.

Develop statistical, machine learning, and deep learning models to measure the impact of pharmaceutical HCP, DTC, and Medical Affairs campaigns. Partner with internal and external clients, provide analytical insights and methodological guidance, consult with sales and market research teams, and coach an offshore analytics team. The role requires applying causal inference, healthcare and pharmaceutical data expertise, and clear communication of findings to clients and executives.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

Leads analytical and scientific strategy for Fetch’s Play and Rewarded Apps businesses. Develops predictive and machine learning models, experimentation frameworks, causal analyses, feature pipelines, and measurement systems using Python and SQL. Partners with Product, Marketing, Engineering, and business leaders to improve monetization, engagement, retention, personalization, and marketplace outcomes. Translates complex findings into strategic recommendations, mentors data scientists and analysts, and establishes analytical best practices.

Own the full machine learning lifecycle for search ranking and retrieval, including model training, deployment, serving, feature pipelines, evaluation frameworks, hybrid lexical-vector retrieval, large-scale embedding inference, query understanding, and permissions-aware multi-tenant search. Collaborate with infrastructure, AI, and backend teams to improve search relevance across ClickUp’s platform.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy; delivers executive dashboards, KPI scorecards, operational reporting, semantic models, and self-service BI. Establishes data governance and metric standards, ensures data quality, partners with Engineering, Product, Finance, Operations, and executives, and translates business questions into actionable analytical solutions. Mentors analytics talent and drives prioritization, adoption, and measurable business outcomes.

Senior backend engineer responsible for setting technical direction, designing scalable distributed systems, building customer-facing web applications, mentoring engineers, and applying machine learning to large-scale healthcare data. The role requires Java, Scala, or Rust expertise, cloud systems experience, technical design leadership, cross-functional collaboration, and adoption of agentic engineering practices.

Leads Alkami’s enterprise and technology risk management program, including risk assessments, control evaluations, mitigation, monitoring, reporting, and escalation. Owns AI, machine learning, and generative AI governance, translating regulatory and risk expectations into practical policies, controls, and review processes. Partners with cross-functional risk owners and supports compliance initiatives involving business continuity, disaster recovery, regulatory management, and incidents. Reports to the Chief Compliance Officer in a fully remote role.

Build and operate production retrieval-augmented generation systems, including embeddings, vector retrieval, grounded language-model generation, citation validation, prompt and schema design, model packaging, serving, rollback, and telemetry. Develop bounded model assistance for narrative extraction with provenance and rule-context tracking. Maintain resilient serving paths in secure government cloud environments, including restricted or air-gapped deployments.

Provides product and technology legal counseling throughout the product lifecycle while operating Horizon3’s global privacy program. Advises Product, Engineering, Security, Compliance, and GTM teams on privacy laws, AI regulation, data flows, DPAs, cross-border transfers, privacy-by-design, incidents, audits, and customer diligence. Drafts playbooks, policies, notices, and contractual terms; supports enterprise negotiations; and delivers practical, risk-based guidance in a fast-moving cybersecurity environment.