Top Machine Learning Jobs in Miami, FL

Own the vision, strategy, roadmap, business case, licensing, lifecycle policy, deployment models, and go-to-market strategy for GridBeats Device Management. Lead customer discovery, product requirements, release planning, cross-functional delivery, sales enablement, and KPI tracking. Partner with utilities, engineering, commercial, marketing, and global sales teams to deliver secure device-management software for grid operators and industrial customers.

Designs and leads scalable contact center architectures using Zendesk, Amazon Connect, and AWS. Defines modernization strategies, oversees integrations, governance, security, and compliance, and mentors presales specialists. Supports discovery, solution design, demonstrations, proofs of concept, presentations, and technical documentation. Collaborates with business leaders, IT teams, vendors, and partners while driving innovation in omnichannel, AI-enabled customer service technologies.

Own the end-to-end architecture, development, deployment, evaluation, and operations of AI and machine learning systems. Build predictive ordering models, agentic workflow copilots, retrieval and evaluation infrastructure, safety guardrails, and responsible AI practices. Prioritize AI initiatives based on business outcomes, establish model governance and production standards, advise product and engineering leadership, and mentor experienced contributors.

Own the strategy, roadmap, packaging, and go-to-market evolution of Pluralsight’s AI, Cloud, and Security learning solutions. Design blended learning programs, define expert perspectives, identify content and lab components, monitor market needs, and use customer and learner data to improve outcomes. Partner with Product, Sales, Marketing, Curriculum, Engineering, and Services teams, support strategic enterprise deals, manage solution benchmarks, and drive adoption, renewals, and revenue growth.

Supports the Data Science and Machine Learning team by analyzing large-scale datasets, identifying anomalies and root causes, developing data validation and normalization methods, and improving the accuracy of derived data products. The intern will evaluate models, translate ambiguous problems into statistical formulations, debug messy data, test hypotheses, and implement maintainable analytical solutions using Python, SQL, and distributed data platforms.

Develop and implement software solutions for automated trading systems, working with quantitative researchers on complex financial challenges.

Design, develop, and deploy software solutions for automated trading systems while collaborating with researchers on technology priorities.

As a PhD intern, you'll design, develop, and deploy software solutions for automated trading, collaborating with the Quantitative Research team.

Lead the development and production deployment of applied machine learning and LLM solutions across product, marketing, and operations. Own ranking, recommendation, filtering, personalization, experimentation, and model evaluation systems. Partner with business and technical teams to identify valuable use cases, establish modeling best practices, and ensure reliable production deployment. Manage and mentor applied ML practitioners while maintaining technical rigor, business alignment, and measurable customer and revenue impact.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

Build scientific advisory and technical enablement programs for enterprise and research partners adopting an AI materials platform. Engage computational chemists and materials scientists, provide technical guidance, lead workshops and demonstrations, create documentation, notebooks, APIs, and reference materials, and relay partner feedback to AI Research, Chemistry, Product, Marketing, and PR teams.

Design, develop, secure, modernize, and support scalable omnichannel APIs for customer-facing web, mobile, contact center, partner, and enterprise applications. Improve API security, resiliency, performance, observability, testing, and reliability. Collaborate with architecture, security, cloud, product, operations, and engineering teams; lead technical delivery, mentor engineers, develop reusable patterns, and provide Tier 3 production support. Apply AI-assisted engineering practices and contribute to API lifecycle management and continuous improvement.

Lead the design, development, deployment, and operation of production AI and ML systems. Build high-throughput Python services and Databricks pipelines, support traditional ML and LLM applications, establish testing and deployment standards, and develop monitoring for quality, reliability, latency, throughput, and cost. Partner with product, research, and responsible AI teams on governance and release criteria. Provide architectural leadership, conduct code reviews, mentor engineers, and resolve complex technical dependencies.

Leads Moss' external communications function, including public relations, media relations, issues and crisis communications, social media, web content, and corporate messaging. Manages PR agency relationships, media inquiries, content development, communications analytics, and team members. Develops and executes strategies to strengthen brand reputation, coordinates cross-functional messaging, oversees digital channels, and uses AI and machine learning tools to improve communications workflows and content quality.

Develop and support robotic process automation solutions across business and operations teams. Responsibilities include designing, developing, testing, deploying, documenting, monitoring, and troubleshooting RPA workflows and components. The role gathers requirements, creates technical designs, integrates solutions with systems such as SAP, supports production deployments, ensures governance and quality, trains users, and participates in on-call support.

Leads client-facing data science and analytics initiatives, translating complex marketing and media data into actionable business narratives. Develops measurement strategies, including test/holdout incrementality, informs acquisition and retention decisions, scales analytics best practices, and manages analyst teams. Partners with Sales, Client Services, Operations, and other stakeholders while supporting predictive and adaptive AI/ML solutions. The role requires strong client communication, strategic leadership, analytics expertise, and proficiency with Python, SQL, Tableau, and related tools.

Build and operationalize machine learning models for large-scale advertising optimization. Responsibilities include designing high-volume services, developing data solutions, training and evaluating models, improving calibration and guardrails, creating offline and online evaluation workflows, and monitoring production systems. The role requires collaboration with product and engineering teams, ownership of reliability and performance, participation in on-call rotations, and deploying scalable, cost-effective ML solutions.

Leads parallel customer AI/ML solution design engagements from discovery through proposal. Architects enterprise solutions involving predictive analytics, NLP, computer vision, and generative AI; provides cost-benefit and TCO analysis; identifies risks and dependencies; and collaborates with Agile, system architecture, and DevSecOps teams to deploy governed, explainable, and monitored AI/ML systems.

Leads the bankruptcy risk solutions product portfolio across the Americas, owning product vision, strategy, roadmap, execution, customer discovery, and commercialization. Partners with Engineering, Data Science, Sales, Marketing, Client Success, and enterprise customers to develop data-driven products, manage backlogs in Jira, support migrations, monitor KPIs, and drive adoption, growth, and product-market fit.

Lead enterprise AWS consulting engagements from architecture through deployment. Design resilient cloud-native, serverless, and containerized solutions; migrate legacy infrastructure; and deliver Generative AI and machine learning workloads using AWS services. Partner with executive stakeholders, build proofs of concept, drive cloud adoption and account growth, mentor engineers and architects, and develop reusable Terraform modules, Well-Architected Review frameworks, and delivery best practices.

Leads and builds the mid-market business development function, owning territory strategy and the full sales cycle for pharma and biotech accounts. Develops and coaches a high-performing BD team, drives pipeline and new-logo acquisition, closes multi-year SaaS deals, and partners with marketing, strategy, product, and commercial operations. Represents the company at industry events and contributes to commercial frameworks, forecasting, segmentation, and coverage models.

Lead technical product strategy and execution for AI-powered learning intelligence infrastructure. Own roadmaps for AI curation, data ingestion, recommendation algorithms, learner profiles, analytics, and platform interoperability. Translate architecture, research, data, and business requirements into technical specifications, backlogs, success criteria, and implementation plans. Coordinate cross-functional stakeholders, manage delivery risks, and ensure systems are explainable, privacy-aware, validated, and aligned with educator and learner needs.

Manage a portfolio of B2B SaaS customers in the HR technology space, driving renewals, expansions, and revenue growth. Build strategic client relationships, conduct business reviews, monitor customer health, manage renewal and sales cycles, introduce products, and advocate for customers internally. The role requires strong sales acumen, analytical abilities, HR technology expertise, and experience influencing HR and L&D decision-makers. It also includes identifying adoption barriers, creating customer feedback loops, and mentoring others.

Own Nabla’s product and EHR partnerships from sourcing through negotiation, integration, launch, and performance tracking. Build scalable partnership processes, negotiate commercial agreements, coordinate cross-functional workstreams, and strengthen relationships with healthcare platforms and health systems. Support corporate development by monitoring adjacent markets, sourcing acquisition targets, developing investment theses, and coordinating diligence. Translate technical and commercial requirements across Product, Engineering, Legal, Finance, Marketing, and Customer Success.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.