Top Machine Learning Jobs in Miami, FL

This role entails using statistics and machine learning to analyze complex datasets, implementing algorithms, and back-testing models. A PhD is required for this position.

Leads AI- and data-driven manufacturing transformation strategies for Life Sciences clients. Advises C-suite and operations leaders, develops AI strategies, business cases, roadmaps, operating models, and responsible AI plans, and oversees large-scale greenfield and brownfield manufacturing programs. Drives digital twin, automation, data platform, advanced analytics, generative AI, and AI/ML deployments from proof of concept through enterprise scale. Builds executive relationships, originates opportunities, manages partner ecosystems, develops manufacturing capabilities, and leads teams, delivery, financial performance, and risk.

Owns BI product strategy for Quote-to-Cash and Order-to-Cash processes. Partners with finance, sales, legal, accounting, product, and engineering teams to identify operational friction, translate financial data needs into BI and AI/ML opportunities, author PRDs and roadmaps, and ensure quoting, contracting, billing, and revenue recognition processes are accurately represented in downstream data products.

Own BI product strategy for Quote-to-Cash and Order-to-Cash financial systems. Identify operational friction, translate finance needs into BI, AI, and machine-learning opportunities, write PRDs and roadmaps, define business requirements, and ensure quoting, contracting, billing, and revenue-recognition processes are accurately represented in data products. Partner with finance, sales, legal, product, delivery, and engineering teams.

Develops predictive and prescriptive models, machine learning solutions, dashboards, and LLM-based chatbots for clinical, operational, financial, and research applications. Collaborates with healthcare, academic, medical, engineering, and business stakeholders to translate data insights into decisions. Responsibilities include the machine learning lifecycle, data products, web applications, documentation, model interpretation, data quality, and cancer epidemiology research communications.

Leads fraud strategy and prevention for a consumer lending product, owning fraud typologies, repayment and chargeback analytics, decision-engine rules, machine-learning risk models, blocking protocols, investigation workflows, and fraud performance metrics. Partners with Credit Risk, Data Science, Engineering, Operations, Legal, and Compliance teams to identify synthetic identity, account takeover, and loan abuse patterns while supporting regulatory reporting and profitable growth.

Lead fraud strategy and prevention for a consumer lending product. Design fraud typologies, analyze repayment fraud, identity spoofing, chargebacks, synthetic identities, and account takeover risks, and own fraud performance metrics. Architect decision-engine rules, deploy machine-learning risk models, develop automated blocking and investigation workflows, and coordinate with Legal and Compliance on suspicious activity reporting and regulatory requirements.

Leads data engineering, Palantir Skywise architecture, and AI solution development for Airbus manufacturing and operational environments. Builds scalable pipelines, analytical workflows, machine learning and generative AI applications, and enterprise data foundations. Oversees Alteryx and Tableau platforms, establishes governance, mentors technical teams, translates business challenges into data products, and supports AI Center of Excellence initiatives. The role is onsite 60% across several U.S. locations and requires approximately 10% travel.

Own fraud performance across cards, ACH, and digital wallets by analyzing fraud vectors, designing product-based controls, optimizing authentication and payment friction, and partnering with Product, Engineering, and Data Science. Conduct payment-rail threat analysis, build and validate experiments, inform fraud-model features, and manage decisioning logic in Sardine or Oscilar. The role requires deep expertise in payment mechanics, advanced SQL and Python analytics, experimentation, and fraud-risk strategy.

Own fraud performance across cards, ACH, and digital wallets by analyzing fraud vectors, payment mechanics, and emerging threats. Design product-based controls, authentication steps, holds, and friction strategies; partner with Product, Engineering, and Data Science; run experiments to measure fraud reduction and conversion impact; and maintain decisioning logic in Sardine or Oscilar. The role requires advanced SQL and Python, payment rail expertise, analytics, machine learning knowledge, and strong cross-functional communication.

Leads mission data integration and engineering initiatives supporting SOUTHCOM’s Common Intelligence and Operational Pictures. Responsibilities include building ETL pipelines, ingesting and transforming data, managing Databricks, Maven Smart System, and War Data Platform solutions, developing machine learning and statistical models, validating data quality, supporting secure interoperability, collaborating with intelligence and operational teams, delivering training, and documenting analytical processes.

Supports sales by discovering client needs, configuring solutions, and delivering compelling demonstrations of SECUTIX’s ticketing and event management platform. Develops RFI and RFP responses, builds trusted stakeholder relationships, communicates product value to technical and executive audiences, tracks opportunities, and provides market and product feedback. The role requires expertise in ticketing, sales, marketing, analytics, live entertainment, sports, cultural arts, or attractions, with travel of up to 40%.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Develop high-performance research platforms and tools that bring trading strategies to life. Work in small teams to design, build, and deploy resilient systems using distributed computing, NLP, and machine learning to solve large-data problems in finance.

As a Quantitative Research Engineer, design, develop, and implement software for automated trading systems using advanced statistical techniques. Collaborate with researchers and prioritize business needs.

Supports AI teams by curating image and video datasets, managing annotation workflows, running model training and evaluation jobs, maintaining benchmarks, documenting results, and analyzing model failures with senior engineers. The role requires foundational Python, machine learning, computer vision, and cloud knowledge, with opportunities to grow into applied modeling or MLOps.

Own and improve Headway’s privacy program across HIPAA, 42 CFR Part 2, state privacy laws, individual rights, incidents, vendor governance, and AI use. Maintain controls, data maps, metrics, training, and operational workflows. Conduct product, vendor, and AI privacy assessments; manage rights requests and incident investigations; coordinate notifications and regulatory submissions; and translate complex requirements into practical guidance for cross-functional teams.

Own the strategy and roadmap for patient-provider search, retrieval, ranking, and marketplace matching. Lead experimentation, objective functions, evaluation, and ML platform foundations while partnering with ML engineers, data scientists, design, clinical teams, and executives. Improve successful, lasting care matches rather than optimizing only clicks or bookings. Balance patient access, provider growth, clinical quality, and payer priorities, while setting the standard for technically grounded product management.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

Serves as the technical bridge between customers, Sales, and technology teams. Leads technical discovery, solution architecture, demonstrations, workshops, proofs of concept, and evaluations using HERE data, APIs, mapping, routing, spatial analytics, cloud, AI, and enterprise integration. Partners with Sales to qualify opportunities, reduce technical risk, develop proposals and reusable presales assets, and guide customers toward location-intelligence solutions.

Analyze malware, binary files, customer detection tickets, and machine learning detections to improve detection efficacy and reduce false positives. Review product detections, assess binary legitimacy, address internal detection questions, and provide insights to data science teams. The role collaborates with technical account managers, incident response teams, data scientists, and malware researchers. Required capabilities include malware analysis, reverse engineering, Python scripting, binary fundamentals, threat assessment, research methods, operating systems, and AI-assisted workflow improvement.

Own the global-health clinical research and evaluation agenda for a medical AI platform. Design validation frameworks for accuracy, safety, multilingual performance, and real-world LMIC use; develop outcome metrics and theories of change; shape point-of-care safeguards; build research partnerships; and translate field insights into product improvements. The role requires practicing clinical expertise, direct LMIC care-delivery understanding, clinical AI evaluation experience, and approximately 25% travel to research and partner sites.

11-week quantitative research internship developing and implementing mathematical and trading models. Responsibilities include model conceptualization, backtesting, live implementation, using unconventional data, and conducting statistical research to refine trading signals and monetization systems.

Conducts supervised postdoctoral research into leukemia and therapy-related myeloid neoplasms. Applies molecular, genetic, computational, and single-cell genomic techniques to investigate cancer biology and develop therapeutic insights. Analyzes sequencing and bioinformatics data, develops analytical models, prepares reports and manuscripts, presents findings, and collaborates across laboratories. A Ph.D. is required; computational biology, single-cell technologies, and leukemia or MDS experience are preferred.