Top Machine Learning Jobs in Jacksonville, FL

The Strategic MSP/MSSP Account Executive will identify and develop partnerships, create strategic plans, and drive revenue growth by engaging with prospective MSP/MSSP partners while collaborating internally to optimize sales strategies and maintain relationships.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Lead data science initiatives to develop and deploy AI solutions that improve business outcomes. Collaborate with cross-functional teams to analyze data, create models, and evaluate performance.

The Enterprise Account Executive will manage the complete sales cycle, build relationships with senior leaders, and drive platform adoption within data-intensive organizations.

The Analytics Lead will leverage data analytics to optimize credit strategies, monitor portfolio performance, and collaborate on credit policy with stakeholders across technical and non-technical teams.

Lead technical program and application support for contact center systems (chat, chatbot, IVR). Partner with product, engineering, and business teams to define scope, document requirements, analyze system logic, identify risks, and implement solutions. Coach junior analysts, drive integration across technology areas, and support GenAI/ML-enabled automation to ensure high availability and performance.

Lead ML feature strategy and engineering for fraud detection, owning end-to-end pipelines, validating features at scale, and translating analytics into production ML systems. Drive tooling and architecture decisions, mentor junior team members, partner cross-functionally with product and risk teams, and apply practitioner-level ML knowledge to detect and prevent account takeover and scam activity.

The role involves building and optimizing a machine learning and simulation platform, supporting MLOps, model deployment, and infrastructure modernization, while mentoring engineers.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

The AI/ML Engineer will design and implement machine learning algorithms, analyze data, collaborate with teams, optimize models, and present findings.

The Engineering Manager, Data Science will lead a team to drive machine learning initiatives in AdTech, focusing on optimization and modeling strategies to enhance client success and operational efficiency.

The role involves designing, building, and deploying computer vision models for vehicle damage detection, covering the entire ML lifecycle and collaborating across teams.

Lead zero-to-one product initiatives to transform knowledge discovery using ML/LLMs. Define vision, roadmap, and success metrics; prioritize trade-offs; partner with Design, Engineering, ML, and Data Science; deliver incremental wins while building an extensible platform and improving prospect and subscriber experiences across devices.

Design, implement, and scale event-driven, distributed backend systems in Python on AWS to process and enrich large-scale document and media datasets. Lead technical projects, mentor engineers, integrate ML/LLM components, optimize performance, maintain observability, and collaborate with Data Science, ML Engineering, and Product to deliver reliable high-throughput content processing services.

Design, build, and optimize event-driven, distributed backend systems and APIs in Python for large-scale content metadata extraction and enrichment. Deploy and operate resilient AWS-based pipelines, collaborate with ML teams to integrate models, and ensure system performance, observability, and data integrity.

Design, build, and scale event-driven, distributed backend systems in Python to extract and enrich metadata at massive scale. Lead architecture, mentor engineers, integrate ML/LLM components, optimize performance, and ensure observability and reliability across AWS-based pipelines.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Develop large-scale Foundation Models for Autonomous Driving & Robotics, collaborating with teams, optimizing model performance, and mentoring junior members.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

Build and operate ML training and serving infrastructure by designing, developing, and launching backend systems. Collaborate across teams to decompose projects, support operations and on-call, create monitoring and metrics, perform code reviews, and contribute to developer velocity and platform reliability.

Lead statistical activities for clinical trials from protocol design through reporting. Provide statistical oversight, collaborate with cross-functional teams and CROs, apply SAS/R (and ML) for analyses, automate processes, and mentor junior statisticians.

Design, develop, and deploy causal inference and predictive models (uplift, synthetic control, DML) for customer loyalty, churn, and LTV. Build foundational data models and scalable features, perform large-scale SQL ETL, operationalize models in cloud, partner with stakeholders, and present insights to drive revenue and loyalty program optimization.

Lead engineering strategy and delivery for customer-facing web and mobile platforms; drive AI-native transformation, increase conversion and speed-to-fund, raise engineering talent and velocity, ensure secure and compliant architectures, partner with product and cross-functional teams to deliver measurable business outcomes.