Top Machine Learning Jobs in Miami, FL

AI/ML Data Contributors will perform data labeling, content evaluation, and user testing to improve machine learning models, working on flexible task-based projects.

Role involves assisting with machine learning model improvement through tasks like data labeling, content evaluation, and participation in testing activities.

As an AI/ML Data Contributor, you will perform data labeling, content evaluation, and user-based testing to improve machine learning models, working both remotely and on-site.

Leads the company’s adoption of AI, analytics, automation, and business intelligence. Builds AI-powered workflows, Excel solutions, natural-language data interfaces, dashboards, forecasts, predictive analytics, and automated reporting using Snowflake, SQL, Tableau, and related tools. Partners across business functions to identify opportunities, improve data quality, automate repetitive processes, and deliver scalable decision-support solutions. Establishes responsible AI practices and advances an AI-first analytics culture.

Clean, standardize, enrich, and import product data into the PIM while maintaining data integrity and governance standards. Oversee bulk ingestion processes, leverage AI and automation, support data model improvements, resolve product listing issues, and contribute to data hygiene and workflow modernization. The role also involves collaborating with client-facing teams, documenting processes, proofreading product data, communicating progress, and adapting to changing priorities.

Own discovery, definition, and delivery of marketplace directory features for Avetta’s B2B SaaS platform. Serve as the customer advocate, conduct research and usability testing, analyze product metrics, prioritize problems, define KPIs, and collaborate with engineering, UX, marketing, and customer success. The role focuses on scalable marketplace and platform products involving search, profiles, matching, recommendations, onboarding, monetization, trusted data, and network effects.

Own and modernize Apartment List’s B2C lifecycle marketing program across email, push, and SMS. Build segmentation, personalization, partnerships, cross-sell, forecasting, experimentation, and measurement systems. Lead analytics, attribution, deliverability, and revenue optimization while remaining hands-on with journeys, templates, dynamic content, audience logic, SQL, and troubleshooting. Manage a two-person team, partner cross-functionally on roadmap priorities, and oversee the Iterable relationship.

Drive new-account acquisition and cloud migrations for AI and infrastructure solutions. Generate pipeline through global outbound prospecting, execute multichannel outreach, manage the full enterprise sales cycle, and close large cloud, infrastructure, and data center deals. The role requires deep knowledge of AI computing, cloud infrastructure, power capacity, cooling systems, financial modeling, and B2B enterprise net-new cloud sales. Candidates work from Akamai’s San Francisco office and collaborate across time zones.

Own technical product marketing for Bloomreach’s APIs, integrations, and agentic AI platform. Translate platform capabilities into positioning, use cases, partner launch strategies, customer storylines, sales enablement, and go-to-market materials. Collaborate with Product, Engineering, Solutions Engineering, partners, sales, and technical buyers. Define launch metrics, coordinate milestones, communicate product value, and evaluate post-launch results.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

The Senior Staff Machine Learning Scientist will lead research initiatives in computer vision and multimodal understanding, develop novel models, and translate research into practical applications while mentoring team members.

Own the strategy, requirements, evaluation, and execution of AI agent products and systems. Partner closely with ML, backend, and mobile engineers to make trade-offs across quality, latency, cost, reliability, and user experience. Define evaluation frameworks, drive prioritization, and ensure AI features are understandable, predictable, safe, and trusted. Translate model capabilities and feedback into product decisions and reliable real-world workflows.

Own the strategy, requirements, evaluation, and reliability of AI agent products. Partner closely with ML, backend, and mobile engineers to make trade-offs across quality, latency, cost, reliability, and user experience. Define offline and online evaluation frameworks, drive product execution, and ensure AI features are correct, predictable, safe, and trusted. Translate model capabilities and feedback into product decisions, roadmaps, and rapid iteration.

Lead the Data Sources product roadmap for document parsing, fraud detection, and bank aggregation. Drive metrics (parse accuracy, fraud catch rate, connection success, SLAs), write specs, engage clients, mentor PMs/engineers, and represent the platform externally. Prioritize document types, extraction models, reliability, and scalability to improve conversion, client satisfaction, and operational efficiency across mortgage, tenant screening, and government use cases.

Lead enterprise AI and transformation engagements—own customer relationships, scope buildable solutions with FDE, drive commercial outcomes and expansion, advise C-suite on AI strategy, and create repeatable, scalable offerings to evolve Tive into an AI-powered intelligence platform.

Build and operate scalable data infrastructure and pipelines for video, imagery, telemetry, sensor, log, and mission data. Create searchable data lakes, curated and versioned ML datasets, labeling workflows, quality checks, lineage, monitoring, and reproducible dataset-generation processes. Partner with autonomy, perception, software, simulation, and field teams to support model training, evaluation, debugging, replay, and deployment workflows.

Build and operate production machine learning training systems on Amazon SageMaker using AWS Trainium. The role focuses on optimizing PyTorch code for Trainium hardware, understanding NeuronCore compilation, diagnosing device-level issues, tuning distributed training for cost and throughput, and delivering production pipelines. The engineer will collaborate directly with client and internal teams in a forward-deployed engineering environment.

Develop reusable ML tooling and libraries, collaborate with data scientists to productionize models, build services and APIs (Docker/Linux), follow engineering best practices, and support project lifecycle from intake to maintenance.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Build and deploy production-grade LLM applications, AI agents, models, and scalable RAG pipelines for investment and business workflows. Architect secure MCP integrations with enterprise systems, establish MLOps and LLMOps practices, optimize model performance and latency, and develop evaluation, monitoring, governance, and rollback processes. Collaborate with data engineering and platform teams to provide secure, governed access to organizational data.

As a Research/ML Engineer, you will translate cutting-edge AI research into product solutions, develop safety and reliability systems, and collaborate with technical teams.

Lead design and implementation of scalable data models and analytical tooling for Consumer Product. Partner cross-functionally to translate business needs into reliable SQL/Python data products, dashboards, and automated pipelines using engineering best practices (CI/CD, testing, version control, monitoring). Drive data-informed strategy and manage long-term infrastructure while supporting ad hoc requests.

Evaluate and author high-quality computer science content to improve AI model reasoning and accuracy. Analyze complex CS problems, create scenarios/datasets, review AI outputs for correctness, and collaborate remotely to refine guidelines and documentation.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.