Top Machine Learning Jobs in Jacksonville, FL

Lead technical strategy and architecture for Azure Storage geo-replication, data protection, failover orchestration, and business continuity. Drive cross-team delivery, operational excellence, telemetry, automation, AI-assisted operations, and represent the team in executive and customer engagements.

Drive go-to-market execution across Nabla’s product portfolio, including positioning, messaging, sales enablement, competitive intelligence, demand-generation partnerships, customer adoption content, and event support. Progress into owning go-to-market strategy for specific products from early definition through launch and adoption, partnering with Product and Engineering and tracking launch success metrics.

Own credit portfolio performance analysis, credit policy, underwriting strategy, risk frameworks, and portfolio growth initiatives across lending products. Partner with Data Science, Product, Engineering, Operations, leadership, and external lending partners to develop predictive models, automated decisioning, analytical tools, dashboards, and reporting. Present strategic recommendations on portfolio risk, borrower behavior, credit expansion, and company-wide risk strategy.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Lead end-to-end healthcare analytics projects, managing scope, planning, execution, client relationships, cross-functional collaboration, risks, and delivery. Provide thought leadership in healthcare analytics and identify business opportunities within client accounts. Required capabilities include quantitative analysis, project management, communication, presentation, SQL/Python/R proficiency, and strategic use of AI tools. Preferred experience includes consulting, cloud analytics platforms, healthcare data regulations, predictive modeling, machine learning, and data visualization.

Leads architecture and development of production-grade mapping and localization ML systems for autonomous vehicles. Designs neural models for detection, segmentation, tracking, depth estimation, 3D reconstruction, and semantic mapping; owns data strategy, evaluation, deployment, and fleet validation. Mentors engineers, drives technical roadmaps, collaborates cross-functionally, and advances vectorized HD mapping, foundation models, and real-time optimization.

Own end-to-end design for complex financial product experiences from exploration through launch and iteration. Translate financial workflows into intuitive customer experiences, set high standards for interaction and visual design, partner with product and engineering, use research and data to guide decisions, contribute to design systems, present work clearly, and mentor teammates. Apply AI-enabled tools thoughtfully while maintaining strong human-centered judgment.

Lead implementations of iQueue for Operating Rooms from discovery through configuration, validation, and rollout. Partner with hospital, clinical, operational, IT, and executive stakeholders to optimize OR workflows, scheduling, staffing, utilization, and access. Responsibilities include data analysis, solution design, product configuration, user acceptance testing, change management, risk monitoring, stakeholder alignment, and go-live readiness. Travel to customer sites is required up to 40–50%.

Design and implement scalable machine learning pipelines for insurance use cases, including classical ML and LLM workloads. Lead production deployments, build reusable ML infrastructure and CI/CD pipelines, and establish observability, testing, model monitoring, drift detection, logging, and rollback standards. Mentor junior engineers, collaborate cross-functionally, manage cloud-based ML systems, and communicate technical findings to nontechnical stakeholders while maintaining insurance data compliance.

Lead initiatives in designing and building large-scale distributed data systems and ML infrastructure, while collaborating across various engineering teams.

Leads an engineering team building and scaling customer-facing integrations for agentic AI products. Responsibilities include setting technical direction, balancing customer delivery with reusable tooling and platform investments, ensuring reliability and observability, improving developer experience, collaborating cross-functionally, recruiting, and coaching engineers. The role requires customer empathy, strong engineering leadership, technical problem-solving, and experience with production systems or integrations.

Assess AI-enabled products and software against regulatory requirements and standards, supporting conformity assessments, CE marking, certification, and client guidance. Review technical documentation, mentor colleagues, develop assessment procedures and tools, contribute to AI testing methodologies, and support accreditation, quality, and continuous improvement activities. The role requires practical AI/ML experience, regulatory knowledge, strong analytical skills, and collaboration across global multidisciplinary teams.

The DS&ML Group Product Manager oversees Data Science and Machine Learning capabilities, defining strategic vision, managing product portfolios, and ensuring delivery aligned with business needs. Responsibilities include developing roadmaps, executing models, scaling functionalities, and mentoring a team.

Own and scale the customer entry-to-purchase lifecycle: design acquisition journeys, build and optimize welcome/abandon/lead conversion automations, implement personalization and gamification, partner cross-functionally, steward list health and deliverability, and run A/B and multivariate testing to improve conversion and engagement metrics.

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

Lead large, cross-functional programs for Netflix Cloud Infrastructure and AI Platform. Shape multi-year strategy, drive migrations and platform adoption, structure ambiguous technical problems into executable plans, execute end-to-end delivery, manage dependencies and risks, influence investment and architecture, and enable ML practitioners to build, train, deploy, and operate models at scale.

Build and operate scalable, highly available ML/LLM serving and compute infrastructure. Own long-term platform vision, deliver zero-to-one projects, partner cross-functionally, optimize performance and capacity, and support production ML models at scale.

Lead design and execution of scalable experimentation frameworks for Netflix Ads. Develop auction-based, biddable media models, solve cold-start measurement problems globally, apply causal inference and ML, partner with product and engineering, and translate analytic results into strategic recommendations to optimize marketplace dynamics and member experience.

Design, productionize, and deploy machine learning forecasting models for ad inventory and campaign performance in low-latency, real-time advertising environments. Build scalable simulation, budgeting, and pacing systems; collaborate with cross-functional teams to deploy models at scale and optimize ad monetization.

Lead design, execution, and optimization of scaled lifecycle campaigns across email and in-product channels. Use marketing automation, behavioral data, and ML techniques to personalize journeys, drive activation, revenue, retention, and cross-sell. Analyze performance, generate insights, and collaborate with Growth, Product, Engineering, Data Science, Analytics, Sales, and Marketing stakeholders.

Lead a team to analyze, optimize, and grow merchant revenue using analytics and AI. Define strategy, set KPIs, deploy ML solutions, drive go-to-market alignment, execute tests, and deliver actionable insights to executives and cross-functional partners.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Design, train, and productionize machine learning models for fraud and identity risk across the full ML lifecycle. Research new fraud types, perform feature engineering and labeling, write production-ready code for real-time decisioning, monitor models, and provide analyses to inform product, sales, and data acquisition.