Top Senior Machine Learning Jobs in Miami, FL

Leads a data science team focused on customer intelligence and personalization across digital products, apps, newsletters, and marketing channels. Develops lifetime value, segmentation, retention, recommendation, attribution, forecasting, and engagement models. Owns product funnel analytics and experimentation, including A/B testing and causal inference. Partners with Product, Marketing, Editorial, Data Operations, and Engineering to translate customer behavior into product improvements, measurable growth, and scalable data capabilities.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Lead the In-Vehicle Experience team at Samsara, focusing on transforming in-cab cameras into intelligent companions. Collaborate with engineering, prioritize features, and ensure client interactions highlight trust and connection. Conduct customer research and integrate AI and ML into product features.

Lead independent validation and ongoing monitoring of complex financial and analytics models, including ALM, loss forecasting, loan transition, corporate finance, and engagement models. Identify weaknesses, communicate findings, guide remediation, and strengthen model governance. Collaborate with Finance, Quantitative Research, Growth Analytics, Internal Audit, Internal Controls, Accounting, and Compliance to maintain Affirm’s model risk framework and address audit, regulatory, and examiner requests.

Conduct quantitative research and statistical analysis on crypto markets to develop, backtest, and implement automated trading strategies. Work with large and unconventional data sets to build valuation models, refine algorithms, and deploy signals in live trading to drive P&L growth.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

Own applied computer vision and machine learning development, translating business problems into scalable solutions. Build, fine-tune, evaluate, and deploy models for object detection, segmentation, classification, video analysis, forecasting, anomaly detection, regression, clustering, and data analysis. Account for camera, sensor, lighting, and edge-device constraints to ensure robust production performance.

Own Secret Detection and Vulnerability Research as a product: define strategy across prevention, detection, validation, revocation, and reporting; ship detection content and intelligence with measurable quality; drive adoption, retention, and revenue; partner with engineering, research, and Field; use AI to accelerate research and productization.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

Leads principal engineers and cross-functional teams delivering business systems, products, and platform enhancements for Verizon’s indirect consumer channel. Responsibilities include managing technology roadmaps, requirements, business cases, vendor relationships, integrations, Agile delivery, stakeholder communications, and CRM, product catalog, and point-of-activation platforms. The role also drives AI-enabled store solutions, KPI performance, customer and partner experience improvements, and organizational change.

Lead client AI transformation engagements from discovery through implementation. Develop AI strategies and roadmaps, apply product management practices to prioritize and deliver solutions, collaborate with engineering and data teams, participate in Agile delivery, mentor consultants, support business development, and ensure successful, timely delivery of AI and analytics solutions.

Lead design, build, and deploy scalable, reliable Java-based microservices in a cloud-native, event-driven AWS environment. Own backend feature design, code quality, automated testing, and FedRAMP release processes. Mentor engineers, participate in on-call/TechOps, integrate AI tools into development, and drive architecture, observability, and cross-team initiatives.

As a Customer Success Engineer, you'll drive adoption and satisfaction for enterprise customers using Deepgram's voice AI technology, fostering strong relationships and aligning technical solutions with business objectives.

Lead and manage the underwriting and credit risk model validation team, perform hands-on independent validations of complex ML models, assess data integrity and performance, identify and remediate model weaknesses, partner on MRM framework improvements, and coordinate with audit, compliance, and controls to meet regulatory standards.

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

Leads technical modernization of the Compliance and AML/BSA/Sanctions program through AI automation and agentic workflows. Responsibilities include designing governed AI solutions, maintaining transaction monitoring and customer risk-scoring models, enhancing sanctions controls, building Python and SQL reporting solutions, integrating compliance tools, supporting AML risk assessments, and partnering cross-functionally to translate regulatory requirements into secure, auditable technology solutions.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Lead and transform the Fraud Intelligence Team by designing scalable operating models, QA and measurement frameworks, and talent programs. Drive labeling quality, operational metrics, and fraud prevention impact; convert frontline fraud observations into insights for Product, R&D, Analytics, and GTM; leverage AI, automation, and tooling; and serve as a cross-functional fraud subject-matter expert while developing early-career talent and measurable business outcomes.

Own the full enterprise sales cycle for Oversee’s leisure travel business, from prospecting through closing. Build relationships with OTAs, agencies, tour operators, and vacation providers; develop pipeline; identify target segments and use cases; refine positioning and sales strategy; lead complex stakeholder processes; and partner with Product and Engineering to convert market insights into a repeatable leisure sales motion.

Own AgentSee’s sales and go-to-market strategy as the first dedicated sales leader. Personally close early enterprise opportunities, refine the ideal customer profile, positioning, pricing, packaging, and sales process, and translate AI capabilities into measurable business value. Build repeatable sales operations and playbooks, provide market feedback to Product and Engineering, and eventually hire and lead the AgentSee sales organization.

Own customer onboarding by prioritizing integration work, developing Python and SQL data pipelines, automating repetitive processes, and leading technical customer calls. Translate CRM and fundraising data into model-ready formats, manage onboarding risks, document processes, support CSMs, and train team members. The role serves as Dataro’s US technical customer representative while collaborating with engineering in Sydney.

Implement metadata governance frameworks for structured and unstructured data in cloud environments. Track data lineage and synchronize metadata across data catalogs, ESRI Kubernetes, and enterprise repositories. Use OpenLineage, Apache Atlas, and related tools to manage metadata and lineage. Enforce DoD metadata policies and NSG standards for geospatial datasets while advising clients and guiding data science teams.

Leads enterprise cybersecurity architecture for AI, cloud, hybrid, and on-premises environments. Designs secure AI architectures, cloud foundational controls, zero-trust identity models, API and data protections, and secure LLM, RAG, agent, and MCP integrations. Establishes governance, reusable security patterns, model-usage procedures, compliance controls, and audit readiness across AWS and Azure. Advises executives and technical teams, mentors security professionals, and drives secure cloud migrations while addressing AI/ML risks such as adversarial attacks, model poisoning, data drift, bias, and data leakage.

Own enterprise data inventory and translate business needs into data models, quality rules, mappings, and acceptance criteria. Partner with data and software teams on pipelines, governance, monitoring, and lifecycle management. Prepare data for agentic workflows, including semantic layers, knowledge bases, retrieval indexes, and evaluation datasets. Develop reusable analytical models, reports, dashboards, and decision-support products while monitoring data quality and communicating limitations and business impact.

Conducts advanced statistical genetics and bioinformatics research on Alzheimer’s disease, related dementias, and Alpha-1 Antitrypsin deficiency. Designs reproducible analyses of genome-wide association, sequencing, genomic, and clinical data using R and Python. Develops machine learning and large language model approaches for disease prediction and risk stratification. Publishes research, contributes to grants, collaborates with principal investigators, supervises trainees, and advances computational genomics methods.