Top Machine Learning Jobs in Miami, FL

Design and implement automated testing frameworks for data platforms, pipelines, distributed processing systems, and machine learning workflows. Develop strategies for data quality, schema integrity, drift, model accuracy, bias, regression, performance, and resilience. Investigate production failures, conduct exploratory testing, embed continuous validation into delivery processes, collaborate across engineering and data teams, and mentor junior SDETs.

Own the strategy, roadmap, and execution of Dave’s promotions and engagement products. Lead ML-driven personalization, experimentation, promotion targeting, app engagement surfaces, and retention initiatives. Partner closely with Engineering, Data Science, Marketing, Operations, Growth, and senior leadership to deliver measurable outcomes. The role requires quantitative rigor, product judgment, technical fluency, and experience building engagement, retention, or loyalty programs in high-volume B2C technology environments.

Conduct computational discovery and optimization of rare-earth-lean and rare-earth-free permanent magnets. Execute DFT and multiscale simulations, develop machine-learning workflows and interatomic potentials, generate high-throughput materials data, and connect atomistic predictions to microstructure and manufacturing processes. Collaborate with experimental teams to validate models, contribute to publications, patents, project planning, and technical presentations, and support junior researchers.

Leads the company’s global Revenue Operations organization, including Field Operations, Marketing Operations, Data Analytics, forecasting, GTM planning, territory and quota design, revenue systems governance, and AI-enabled automation. Partners with the CFO, CRO, CMO, and Board to improve forecast accuracy, pipeline health, sales productivity, retention, data reliability, and Salesforce and RevTech execution. Builds and develops a high-performing organization supporting growth from approximately $550 million to more than $1 billion in ARR.

Manage multidisciplinary marine sensor datasets, organize and quality-control data, and develop, train, test, and validate AI/ML models for fisheries science. Support sensor-data collection, annotation, cloud repositories, scientific products, proposals, stakeholder engagement, and collaborative NOAA projects. The role may involve field activities and travel and is contingent on contract award.

Leads a span of approximately 20 engineers while shaping hiring, onboarding, career development, staffing, and engineering enablement across the AI practice. The role is also approximately 50% billable client delivery, providing executive advisory on AI software development lifecycles, governance, agentic deployments, MLOps and LLMOps pipelines, cloud platforms, observability, and production resilience. Requires extensive engineering leadership, organizational scaling, consulting, GCP, and production AI/ML deployment experience.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Build backend services, APIs, data-processing systems, and frontend features for Northbeam’s marketing intelligence platform. Work on experimentation, near-real-time data infrastructure, AI-powered workflows, data integrations, RBAC, and end-to-end product delivery. Collaborate with data scientists, product managers, designers, and senior engineers while contributing across backend, frontend, pipelines, and infrastructure in a fast-moving environment.

Leads and builds Instructure’s decision science function, including hiring, team development, roadmap ownership, experimentation, causal and predictive modeling, executive insights, and data literacy. Partners with product, engineering, marketing, finance, and executives to guide product, pricing, retention, go-to-market, and operational decisions. Establishes analytical standards, supports data infrastructure, and ensures ethical, privacy-conscious use of student data.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages agile sprint planning, release delivery, DevOps metrics, and cross-functional project plans. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, manages team members, and ensures reliable, well-governed data solutions for business intelligence and reporting.

Leads end-to-end data architecture, engineering pipelines, ETL, data modeling, quality monitoring, and Tableau BI platform operations. Partners with stakeholders to define requirements, manage project plans, lead agile sprints, and deliver reliable data solutions. Establishes governance, security, performance, and continuous improvement practices while developing AI-enabled analytics capabilities. Mentors data engineers and manages team performance, documentation, dependencies, and delivery across multiple workstreams.

11-week software engineering internship building high-performance, large-data research platforms and trading tools. Work in small teams on system architectures, distributed systems, NLP and ML, and web frameworks while collaborating with senior engineers.

Lead and coach the engineering team building AutoReview, an AI-powered mechanical design review product. Partner with Product on roadmap planning, feasibility, estimation, prioritization, and timelines. Maintain technical context to identify risks and dependencies, guide initiatives involving DFM checks, assembly analysis, and bulk workflows, and collaborate with applied ML teams. Establish clear ownership, support engineer autonomy, and help the team navigate evolving customer needs and AI technologies.

Leads enterprise AI initiatives from use-case prioritization through deployment and adoption. Translates business needs into AI solution specifications, manages cross-functional delivery teams, oversees budgets, timelines, resources, and risks, and measures business value and ROI. Advises senior stakeholders on AI roadmaps, governance, and emerging technologies while supporting team performance and talent development.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

This 12-week graduate internship involves developing healthcare analytics frameworks, performing exploratory analysis and statistical modeling, analyzing complex datasets, and building predictive or prescriptive models. Interns may apply machine learning, NLP, and responsible AI techniques, create dashboards and visualizations, communicate insights to business and clinical partners, and document reproducible methods. The role is primarily on-site in Louisville or designated hub locations and focuses on improving healthcare quality, outcomes, affordability, access, and member experiences.

Lead Amazon performance marketing strategy and execution, manage Sponsored Ads and DSP, collaborate with agencies and cross-functional teams, optimize campaigns and budgets, analyze performance with AMC/analytics, and measure commercial impact to drive ROAS and customer acquisition.

Develop and deploy scalable AI and machine learning applications, including forecasting, anomaly detection, and natural language processing solutions. Design software architectures, troubleshoot full-stack issues, participate in technical design sessions, and collaborate with cross-functional teams. The role requires production machine learning experience, strong programming skills, and the ability to learn evolving AI technologies. Preferred experience includes web service frameworks, distributed systems, and Azure cloud computing.

Leads presales technical strategy for enterprise customers, partnering with account executives and cross-functional teams. Responsibilities include discovery, solution design, narrative demos, proof-of-concept workshops, technical validation, multi-product positioning, customer technology roadmaps, and translating customer insights into product improvements. The role focuses on AI-driven scenario planning, data modeling, enterprise planning, and value-based selling.

Owns US customer and commercial operations for a healthcare hardware and AI company. Responsibilities include forecasting camera demand, managing manufacturing partners and inventory, launching repair and replacement services, executing and negotiating customer contracts, managing legacy obligations, creating SOPs and policies, and automating operational workflows with AI tooling. The role is remote, US-based, with occasional travel for customer engagements and team offsites.

Own adoption, retention, and expansion across a pooled portfolio of scaled customers through digital-first programs. Provide technical API integration guidance, troubleshoot issues, run demos, and create reusable enablement assets. Design automated customer journeys, webinars, self-service resources, and workflows that accelerate activation. Monitor usage and health data, identify expansion opportunities, report KPIs, and build scalable systems that reduce recurring manual work.

Lead CX data intelligence initiatives using SQL, dashboards, analytical models, and AI/LLM techniques. Analyze customer journeys, operational metrics, churn risks, and retention indicators; develop insight agents and self-service reporting; define CX health metrics; and translate findings into strategic recommendations, process improvements, and experiments. Partner with operations, support, product, data, and engineering teams while maintaining data quality, governance, and documentation.

Lead the architecture, development, deployment, and operation of production-grade AI platforms and applications. Build scalable cloud-native services using generative AI, LLMs, APIs, microservices, data pipelines, and evaluation frameworks. Provide technical leadership, mentor engineers and data scientists, establish engineering standards, and ensure AI systems are reliable, secure, observable, compliant, and cost-efficient.

Leads responsible AI and model governance while building and deploying healthcare fraud, waste, and abuse models. Establishes standards for documentation, monitoring, bias, drift, validation, and production readiness. Evaluates generative AI use cases, develops policies, validates large-scale claims models, and presents methodology and controls to partners, auditors, and investigators. Works remotely with data scientists, BI developers, and healthcare subject-matter experts.