Top Remote Machine Learning Jobs in Tampa, FL

Lead strategy and execution for enterprise and mid-market GTM across multi-unit restaurant brands. Drive C-suite engagements, modernize sales motions, scale AI-powered forecasting, partner with Product/Engineering, and represent Toast in industry forums to win large, complex contracts.

Lead strategy and execution to attract, engage, and retain a global community of Pluralsight authors. Build author communications, engagement programs, communities, recognition, and events. Elevate author visibility (Author Fellows), integrate author expertise across Sales, Marketing, Customer Success, Product, and Learning Experiences, and lead a multidisciplinary team to drive measurable business impact.

Lead a global product team building automation-first, AI-assisted investigation and agent-facing tooling for AML, KYC, EDD, and sanctions screening. Define roadmap, metrics, vendor strategy, and regulatory controls to improve investigation quality, cycle time, and operational efficiency while ensuring security, privacy, and auditability.

Advise clients on antitrust eDiscovery compliance using machine learning and generative AI. Build and validate TAR/AI models (Epiq AI, Relativity, Reveal, Brainspace), craft LLM prompts, draft defensible protocols, train/transfer workflows to review teams, and support client-facing, pre-sales, and thought-leadership activities. Must manage multiple projects and meet deadline-driven schedules.

Partner with healthcare clients to discover, design, and deliver production-grade AI/LLM solutions. Architect cloud-native systems, build and deploy ML/LLM applications, prototype rapidly, troubleshoot distributed systems, and lead client engagements to drive measurable business outcomes while collaborating across product, platform, and engineering teams.

Lead B2B activation marketing to drive patient starts from commercial referral channels by building scalable systems, attribution frameworks, and repeatable playbooks. Implement MarTech and data-driven strategies, run A/B and incrementality tests, automate campaign execution using AI/ML and LLM tools, and templatize activation campaigns while managing and growing a cross-functional team.

Lead design and implementation of Headway's insurance systems (eligibility, pricing, matching, claims). Build data-intensive, ML-powered platforms and automation, mentor engineers, write high-impact code, and improve accuracy and reliability across state and payer complexity to scale insurance-covered therapy.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

Conduct research on AI tokenomics by evaluating model architecture trends, building simulation models for training and inference efficiency across hardware and workloads, diligencing companies using model tokens, and forecasting economic impacts for investors, corporates, and policymakers.

Design, build, and operate hyperscale Azure availability and control-plane services to achieve high VM SLAs. Lead architecture, author designs, implement reliable code, run livesite as DRI, and integrate predictive ML/AI models and generative AI for diagnostics and automated root-cause analysis.

Leads technical discovery, enterprise solution architecture, technology assessments, and implementation roadmaps for consulting clients. Designs architectures across applications, data, cloud, integration, and enterprise technology; advises executives on technology decisions, scalability, security, cost, and risk. Guides technical teams, resolves complex delivery challenges, develops proofs of concept and reusable standards, evaluates emerging technologies, and supports proposals, estimates, statements of work, and new business development.

Own end-to-end analytics delivery for a client POD, translating business priorities into workplans, coordinating cross-functional teams, governing delivery and revenue KPIs, and managing risks through resolution. Perform hands-on data profiling, validation, reconciliation, exploratory analysis, automation, and quality reviews using SQL, Python, Databricks, and Excel. Create executive-ready KPI, revenue, and business-impact narratives, prioritize initiatives, support business cases, and identify opportunities for analytics automation and AI-enabled improvements.

Conduct quantitative analysis on large datasets to answer business questions, identify patterns and anomalies, and support predictive and machine-learning model development and validation. Responsibilities include data-quality evaluation, visualization, exploratory analysis, diagnostics testing, and assessing model suitability under supervision. The role also develops programming, statistical, data-literacy, communication, and business skills while collaborating primarily with managers and immediate team members.

Lead development of Root’s machine learning platform for insurance pricing. Build and operate feature pipelines, model training and orchestration, registries, validation, serving, observability, and reproducibility tooling. Partner with researchers to convert experimentation into reliable production systems, explore LLM-enabled data science automation, drive technical roadmaps and execution, and mentor engineers while maintaining high standards for reliability and correctness.

Lead the architecture and development of Root’s machine learning platform for insurance pricing. Build infrastructure for feature pipelines and stores, reproducible training and orchestration, model registries, serving, validation, observability, and research-to-production workflows. Establish versioned contracts and technical standards, automate workflows using LLMs, write and review critical code, influence cross-team strategy, and mentor senior engineers. Collaborate closely with data scientists and researchers to create scalable, reliable, and reproducible ML capabilities.

Coordinates data platform incident response, triage, escalation, root cause analysis, problem management, post-incident reviews, and operational improvements. Maintains runbooks, documentation, metrics, dashboards, and stakeholder communications. Implements and monitors security controls, supports security incident response, vulnerability remediation, compliance audits, regulatory reporting, disaster recovery exercises, and cross-functional coordination across data, cloud, infrastructure, DevSecOps, application, and governance teams.

Own Mirantis’s marketing strategy and repositioning from private cloud infrastructure to full-stack AI infrastructure. Lead brand narrative, analyst and media relations, enterprise and CSP/Neocloud marketing motions, ecosystem partnerships, demand generation, and measurable market-impact initiatives. Reshape and develop the marketing team, collaborate with Sales, Product, CEO, and business development, and build credibility across the Kubernetes, AI infrastructure, GPU, and cloud-native ecosystems.

Develop and deploy predictive and causal models for personalization, retention, monetization, and product strategy. Design experimentation and measurement frameworks, analyze user and marketing behavior, quantify business impact, and translate complex findings into strategic decisions. Collaborate with Product, Engineering, Marketing, and Data Product teams to build scalable data solutions. Mentor peers, promote rigorous model validation and governance, and apply Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to production-ready data science initiatives.

Leads strategic, full-cycle technical recruiting for Engineering, including software, cloud, AI/ML, data, cybersecurity, quality, and product roles. Partners with senior engineering leaders on workforce planning, hiring strategies, skills-based criteria, sourcing, structured interviews, and talent pipelines. Uses market intelligence and recruiting analytics to improve hiring quality, candidate experience, and time-to-fill while coaching hiring managers and driving continuous process improvement.

Lead the design and implementation of large-scale AI and machine learning systems for Yahoo Search across multiple teams. Define data strategies, evaluation frameworks, and scalable production-serving approaches. Drive technical alignment among engineering, product, design, and research teams; guide adoption of large language models, vision models, generative AI, and agentic AI; and oversee data analysis informing strategic product decisions.

Develop threat models, statistical verification algorithms, calibrated tests, and production verification services for decentralized training and inference on untrusted heterogeneous hardware. The role focuses on detecting incorrect model outputs, malicious training behavior, free-riding, poisoning, data extraction, and reward manipulation while controlling false positives and negatives. Candidates need deep statistics and probability expertise, experience shipping or publishing calibrated decision systems, and familiarity with statistical testing, re-execution, cryptographic proofs, and trusted hardware.

Provides clinical expertise, customer support, product demonstrations, training, onboarding, and certification for Transpara Breast AI across the USA and Canada. Supports research initiatives, clinical adoption, customer engagement, NPS tracking, workshops, webinars, educational content, case curation, and customer feedback resolution. Collaborates with Sales, Customer Success, Marketing, Engineering, Innovation, and Product Management teams. Requires medical imaging experience, preferably in mammography or breast imaging AI, strong communication and training skills, and approximately 20% travel.

Drive enterprise sales growth across North American banking, credit card, and financial services accounts. Develop strategic account and business development plans, build customer and partner relationships, close complex multi-million-dollar technology deals, and engage C-level stakeholders. Represent the company at industry events, collaborate with commercial and leadership teams, manage pipeline forecasting and reporting, and apply MEDDPICC methodology. The role focuses on cybersecurity, fraud prevention, authentication, and SaaS or cloud solutions, with 50% domestic and international travel.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Architect and deploy scalable AI/ML systems for an enterprise modernization program. Responsibilities include designing end-to-end ML pipelines, implementing MLOps and CI/CD practices, managing cloud-based multi-tenant and multi-region workloads, automating infrastructure with IaC, developing secure ML services and APIs, validating and optimizing models, supporting responsible AI practices, documenting architecture, tracking performance metrics, and advising teams on third-party ML tools and platforms.