Top Machine Learning Jobs in Jacksonville, FL

Lead architecture and implementation of large-scale generative, agentic, recommendation, and personalization ML systems. Build end-to-end ML platforms (data pipelines, training, deployment, monitoring, governance), define technical strategy and reusable patterns, and partner with Product, Engineering, Data, and Design to deliver production AI experiences for millions of customers.

Design, develop, and deploy machine learning models and scalable MLOps pipelines. Collaborate with data engineers and product teams to build, monitor, and optimize production ML systems, document experiments, and identify ML opportunities.

Design, build, and operate ML infrastructure for training, evaluation, deployment, and inference. Improve reliability, scalability, latency, throughput, and cost. Create pipelines, observability, benchmarking, and tooling to enable fast experimentation and productionization of models while diagnosing regressions and bottlenecks.

Design, evaluate, and productionize generative AI/ML solutions (RAG, agents, embeddings, retrieval). Build evaluation frameworks for hallucination detection, benchmark LLMs, optimize prompts/models, create datasets, fine-tune models, and collaborate to deploy and monitor enterprise-scale GenAI systems.

Build and maintain backend and full-stack marketplace features while supporting ML Ops infrastructure (model deployment, feature stores, real-time pipelines, monitoring). Collaborate with data scientists to bring models to production, write well-tested code, address performance and scalability issues, and contribute to technical planning and cross-functional communication.

Build and maintain full-stack marketplace features and support ML Ops infrastructure (model deployment, feature stores, real-time pipelines, monitoring) on Chalk and Vertex AI. Collaborate with data scientists to productionize models, write well-tested code, address performance/scalability issues, and contribute to technical planning.

Lead development of scalable AI/ML capabilities for Mail Intelligence: build LLM- and ML-based personalization, data pipelines, tools, and observability. Mentor engineers, collaborate cross-functionally, run A/B tests, implement feedback loops, and balance cost/quality/speed tradeoffs to deliver production-grade solutions.

Design, build, and deploy scalable distributed deep learning pipelines and infrastructure. Optimize hardware utilization, profile and benchmark training, manage large datasets, and enable reproducible ML workflows in cloud environments. Collaborate with ML scientists and engineers to align pipelines with research goals and drive performance, reliability, and operational best practices for large-scale model development.

Paid 3-month remote contract for students or recent grads to build and test LLM-based solutions using the Token Factory inference platform. Assist senior SAs with prompt engineering, model selection, benchmarking, and inference optimization. Run performance and quality experiments and contribute to internal tooling and automation. Mentorship provided; strong performers may be considered for full-time roles.

Design, train, and deploy high-throughput ML/NLP models and pipelines at consumer scale. Build resilient production model services, migrate ML infrastructure to public cloud (GCP), process massive distributed datasets, integrate AI-driven workflows, and collaborate cross-functionally to embed personalization into Yahoo Mail while maintaining code quality and performance.

The role involves leading ML strategy, architecting systems, designing algorithms, collaborating with teams, and mentoring engineers in AI for enterprise analytics.

The role involves applying AI/ML techniques to develop model systems for healthcare, collaborating across teams, and conducting R&D.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

Lead the development of device intelligence and fingerprinting systems by designing backend services, collaborating on integrations, and applying advanced machine learning algorithms to enhance security and combat fraud.

Lead and grow the AI/ML Engineering team, manage core AI/ML platform development, ensure production-grade service operational excellence, and develop product strategies.

Own end-to-end ML lifecycle for pricing and underwriting: improve model accuracy and coverage, reduce infrastructure cost, deploy and monitor production models on AWS, run experiments/backtests, and collaborate with domain experts to inform model improvements and product APIs.

The Lead Machine Learning Engineer will design and evaluate models for autonomous driving systems, mentor junior engineers, and utilize large datasets to improve ML capabilities at May Mobility.

The Principal Machine Learning Engineer will fine-tune and optimize LLMs for various applications, develop AI products, and enhance ML infrastructure in collaboration with cross-functional teams.

Build and productionize large-scale ML models and pipelines for query understanding and personalization. Work on autocomplete, query tagging/NER, query expansion, intent modeling, and LLM-powered natural-language search. Collaborate cross-functionally to design, deploy, and monitor batch and real-time systems, leveraging modern ML infrastructure and tooling to improve search relevance across Airbnb products.

Design, fine-tune, evaluate, and deploy LLM-driven, user-facing browser features using RAG, embeddings, prompt engineering, and modern ML tooling. Lead end-to-end model lifecycle, run production experiments, collaborate with product and engineering, and document and review work with an emphasis on privacy, latency, and usability.

Lead MLOps for production ML lifecycle: deploy and serve models, build scalable reproducible pipelines for large protein-design workloads, implement CI/CD and observability, optimize GPU/accelerated compute, and enable self-service tooling for researchers.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Lead research and development of longitudinal health trajectory models and next-best-action decision algorithms. Translate member data into deployable, clinically meaningful models, set evaluation standards, mentor scientists, and collaborate with product, engineering, and clinical teams to productionize and monitor ML solutions that optimize interventions and health outcomes.

Lead client-facing architecture and delivery of production-grade GenAI systems. Design scalable LLM, agentic, RAG, recommender, and LTR solutions, manage client engagements, mentor ML engineering teams, and oversee project lifecycle from discovery to deployment and monitoring.