Top Machine Learning Jobs in Jacksonville, FL

Owns CSL Plasma’s target-state business architecture, capability models, value streams, architecture decisions, integration standards, and technology roadmaps. Translates business strategy and regulatory requirements into architecture blueprints across digital products, while governing AI, automation, data, and integration patterns. Partners with product, engineering, enterprise architecture, security, privacy, and compliance teams to drive committed designs, architectural consistency, and migration sequencing across a complex regulated environment.

Leads engineering standards, architecture governance, platform strategy, technical quality, security and compliance engineering, and production AI governance across CSL Plasma’s digital products. Manages cross-domain technical teams, sets cloud and integration direction, owns shared engineering platforms and data architecture, and ensures reliable, scalable, regulated software delivery. The role also drives AI-assisted development, production AI operations, engineering metrics, talent development, and alignment with enterprise architecture and cybersecurity.

Own end-to-end client relationships for a book of B2B SaaS customers: drive adoption, engagement, retention, and expansion of AI-powered contract drafting tools. Lead onboarding, training, large-scale rollouts, and translate customer feedback into product improvements while designing scalable CS processes and tracking health metrics to demonstrate value.

Leads healthcare-focused data science projects from conception through deployment. Develops machine learning, NLP, deep learning, and generative AI models using Python and Google Cloud technologies. Analyzes healthcare data, collaborates with clinicians and cross-functional teams, presents findings to stakeholders, establishes AI best practices, and mentors junior data scientists.

Collects, processes, and analyzes data from multiple sources using R, Python, and SQL. Develops reports, visualizations, analytical methods, and data-driven insights while applying statistical and introductory machine learning techniques. Collaborates with internal and external stakeholders, documents project progress in JIRA, debugs code, clarifies requirements, and communicates findings through reports and presentations.

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.

Supports financial and enterprise risk management through quantitative analysis, market and liquidity risk analytics, deposit modeling, stress testing, balance sheet analysis, and automated reporting. Develops dashboards, risk indicators, forecasting tools, and quantitative models using Python, SQL, Excel, and business intelligence platforms. Researches economic trends, identifies emerging risks, improves reporting processes, and communicates analytical findings to Treasury, Finance, Credit, Risk, and executive stakeholders.

Define the vision, strategy, and roadmap for AI-powered products. Lead use-case evaluation, requirements, experimentation, development, deployment, adoption, and continuous improvement across Generative AI, LLM, RAG, and agentic solutions. Collaborate with engineering, data science, design, and business stakeholders; establish success metrics; manage AI risks; and communicate product value, progress, and strategy to leadership and clients.

Lead enterprise data and AI architecture across marketing, customer engagement, digital, sales, and analytics. Design scalable cloud data platforms, reusable data products, semantic models, real-time and batch architectures, governed AI capabilities, machine learning systems, generative AI, agents, recommendations, and personalization. Establish standards for governance, security, privacy, quality, lineage, and observability. Provide technical direction, lead architecture reviews and roadmaps, partner with engineering and data science teams, and mentor architects and engineers.

Leads Blue Team capabilities for large-scale federal cyber exercises. Designs defender workflows, hunt content, evaluation criteria, training objectives, and operational playbooks; coordinates customers, red teams, control cells, and account teams; facilitates exercises and after-action reviews; translates findings into detection improvements and remediation plans; and mentors cybersecurity personnel. The role requires cloud, hybrid environment, threat hunting, detection engineering, incident response, and cybersecurity exercise expertise, plus extensive customer engagement and 50–75% overnight travel.

Lead the architecture, development, deployment, and reliability of cloud-based customer experience platforms using AWS, Genesys Cloud, and Salesforce Service Cloud. Design integrations, data pathways, CI/CD pipelines, and infrastructure automation while ensuring security, compliance, scalability, and performance. Serve as a technical subject matter expert, guide global agile teams and vendors, coach engineers, establish DevOps practices, and troubleshoot critical production issues.

Own the design, adaptation, integration, release, and production rollout of tailored AI solutions for a large client. Collaborate with distributed engineers, contribute client-specific changes to a shared codebase, ensure reliability in a regulated environment, serve as the client’s technical contact, and resolve complex production issues independently.

Leads and develops a data science team applying statistics, machine learning, causal inference, and AI to business problems. Establishes standards for experimentation, model governance, validation, responsible AI, and reproducibility. Partners with business, engineering, BI, and IT stakeholders to prioritize analytics initiatives, translate business questions into solutions, deploy models into production, and communicate actionable insights to executives and nontechnical audiences. Oversees career development, stakeholder adoption, and strategic alignment.

Leads transformation and business management for enterprise anti-money laundering detection, case management, and customer risk assessment. Oversees financial-crime compliance portfolios, policies, regulatory change implementation, risk reporting, remediation, issue escalation, and control enhancements. Partners with business units, control functions, and second- and third-line defense teams while advising senior leaders and challenging compliance activities and remediation plans.

Lead enterprise security operations, resilience, incident response, threat intelligence, cloud security, IAM, and risk management. Serve as deputy to the CISO and primary incident commander while advising executives on cyber risk. Build and scale multidisciplinary security teams, modernize cloud-native defenses, embed security-by-design across products and AI/ML systems, and establish metrics-driven governance, automation, regulatory alignment, and business-focused security strategies.

Teaches undergraduate and graduate artificial intelligence courses in an online and blended format. Develops syllabi, lesson plans, course materials, assessments, and student feedback aligned with university objectives. Uses online technologies and course software to facilitate learning, integrates responsible AI practices, and evaluates appropriate student use of AI tools. Requires a doctorate, applied AI experience, AI-enabled software development expertise, strong communication skills, and commitment to ongoing faculty development.

Senior Product Designer responsible for creating high-craft, 0-to-1 AI recruiting and talent product experiences. The role involves visual and interaction design, rapid iteration, prototyping, design-system development, production-ready delivery, and experimentation with generative AI. Candidates should have strong Figma expertise, exceptional visual design skills, experience shipping SaaS, enterprise, or AI products, and a portfolio demonstrating creative range and execution.

Leads data engineering teams while designing and optimizing scalable batch and real-time data pipelines on cloud platforms. Responsibilities include developing PySpark and Spark workflows, orchestrating Airflow DAGs, managing cloud infrastructure, implementing CI/CD, improving data quality and performance, and mentoring engineers. The role collaborates with clients, product, architecture, and program management teams, while applying generative AI and DevOps practices to improve engineering productivity and operational efficiency.

Evaluate enterprise data architectures, governance frameworks, integration patterns, ownership models, and data quality practices. Design target-state architectures supporting analytics and AI, define governance operating models, assess data flows, develop quality controls, and create strategic roadmaps and executive recommendations. Collaborate with business and technology stakeholders on data modernization, cloud migration, AI enablement, and enterprise data management initiatives.

Own Fetch’s on-platform advertising measurement strategy, including event taxonomies, attribution, incrementality, reporting, and data pipeline standardization. Lead migration to standardized ad event infrastructure, define measurement methodologies such as multi-touch attribution and geo lift, and partner with engineering, data science, advertisers, and third-party measurement providers. Establish scalable measurement standards and ensure new ad products are instrumented and attributable from launch.

Design the full Search experience across organic and sponsored results, including typeahead, ranking, filters, empty states, and monetization. Establish design principles and components, partner with product, research, data science, and ML engineering, and use experimentation to improve engagement, relevance, and revenue. The role requires substantial search or discovery design experience, strong product craft, consumer instincts, and comfort designing alongside AI and monetization teams.

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.

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.

Leads the technical direction and execution of a machine learning team focused on personalization, relevance, ranking, search, and advertising. Designs, scales, and operates production ML systems, including training, evaluation, deployment, and serving infrastructure. Partners with product, data, and engineering stakeholders, drives architecture and system design decisions, mentors engineers, and uses AI tools to accelerate development and explore LLM-powered features.

Leads Propel America's product and technology strategy, including the Fellow Portal, AI integrations, technology roadmap, software development, Salesforce, infrastructure, data systems, cybersecurity, and vendor relationships. Manages technical staff and outsourced IT providers, oversees architecture, QA, releases, compliance, data privacy, and disaster recovery, and partners cross-functionally to translate organizational needs into scalable products and technology solutions.