Top Senior Machine Learning Jobs in Tallahassee, FL

Lead Runpod’s Americas sales organization and revenue number across new business, expansion, and renewals. Manage and coach Account Executives and Account Managers, oversee forecasting and pipeline, lead complex enterprise deals and contract negotiations, build regional go-to-market strategy and sales playbooks, and convert product-led demand into revenue. Partner with Marketing, Product, Legal, and Finance while improving sales efficiency through AI and developing a remote-first team.

Designs, builds, scales, and operates distributed CI/CD services supporting millions of builds, tests, and releases. Develops code for Azure-based infrastructure spanning virtual machines, containers, and custom lab environments. Responsibilities include requirements analysis, architecture and design documentation, estimation, debugging, optimization, operational playbooks, on-call incident response, monitoring, reliability improvements, and mentoring engineers.

Designs and implements enterprise cloud and AI solutions for unstructured data workloads. Responsibilities include building ingestion pipelines, API integrations, document extraction and HITL workflows, tuning machine learning models, benchmarking performance, and ensuring security, availability, disaster recovery, and data isolation. The role also supports customer developers, leads technical proofs of concept, mentors engineering teams, develops reusable blueprints, and provides feedback to product engineering.

Leads the full lifecycle of clinical NGS diagnostic algorithms for oncology and liquid biopsy applications. Responsibilities include algorithm design, analytical validation, design control, regulatory readiness, variant calling, QC metric development, probabilistic modeling, machine learning, and validation pipeline automation. The role serves as technical authority for algorithm changes, supports regulatory submissions, and develops solutions for low-input, fragmented, and ultra-low-frequency genomic variants.

Serves as a permitting and plan review domain expert for engineering, product, design, data, and go-to-market teams. Reviews permit applications, plan sets, submittals, and AI outputs; interprets building codes; identifies missing or noncompliant information; defines approval and revision criteria; labels examples for ML systems; synthesizes jurisdictional requirements; and develops rubrics, checklists, documentation, and benchmarks. Requires approximately 5+ years of hands-on permitting or plan review experience and fluency in applicable building codes.

Leads the Product Acquisition engineering team and drives technology strategy, architecture, software delivery, DevOps maturity, platform adoption, vendor management, and operational excellence. Partners with product managers on roadmaps and business outcomes, ensures engineering standards and production support, and coaches, recruits, and develops engineers and consultants. Requires strong software development, product operating model, project management, stakeholder communication, and technical leadership experience.

Leads customer and marketing analytics strategy, managing a small analytics team and partnering with executives to improve acquisition, retention, engagement, conversion, and customer experience. Develops KPIs, scorecards, journey reporting, cohort analyses, campaign measurement, segmentation, and A/B testing programs. Oversees scalable analytics frameworks, data quality, governance, architecture, and integration across customer touchpoints. Applies advanced analytics and predictive modeling to deliver actionable insights and presents recommendations to senior leadership and board-level audiences.

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.

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.

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.

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.

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.

Leads an enterprise Account Executive team across a U.S. region, owning pipeline, revenue, forecasting, account planning, sales processes, onboarding, coaching, and growth targets. Supports strategic customer deals, partners with marketing on account-based campaigns, builds utility-industry relationships, and represents Neara at customer sites and industry events. The role also develops user groups and customer feedback loops to inform product direction.

Lead research and development for Harris School Solutions’ K12 products, managing 40+ onsite, offshore, and remote employees. Set product and technical roadmaps, scale AI-assisted development, deliver AI-enabled capabilities, oversee cloud and Microsoft-stack engineering, and establish responsible AI governance. Manage budgets, P&L considerations, portfolio delivery, hiring, coaching, performance, and executive communication while improving development productivity, quality, and operational efficiency.

Leads enterprise product evaluations, implementations, integrations, and adoption initiatives. Manages cross-functional projects involving AI, automation, system modernization, and third-party platforms. Translates business requirements into implementation plans, develops business cases and ROI analyses, coordinates vendors and stakeholders, prepares executive communications, assesses risks, and supports organizational change and user adoption.

Owns the intelligence platform roadmap for enterprise healthcare engagement, including decisioning, orchestration, integrations, interoperability, identity and consent management, and scalable deployment. The role defines strategy and requirements, prioritizes platform investments, leads partner integrations, establishes success metrics, and coordinates Engineering, AI/ML, Data Science, Security, Design, and client teams. It also embeds healthcare compliance, security, and responsible AI throughout the product lifecycle.

Provides strategic finance leadership to venture-backed AI and SaaS startups. Responsibilities include monthly close oversight, budgeting and forecasting, capital planning, treasury management, board reporting, investor updates, fundraising, M&A support, cap table analysis, revenue cycle management, cash flow planning, controls, audit readiness, and advising founders on financial strategy.

Build edge-focused full-stack product experiences integrating machine learning and autonomy for frontline defense users. Collaborate with designers, infrastructure teams, product teams, and ML engineers to create intuitive applications, efficient edge-deployed application stacks, and internal modeling APIs. Develop maintainable software using strong engineering practices, including design documents, code reviews, and style guides.

Leads analytics and reporting for Microsoft’s Cloud Solutions Provider authorization ecosystem. Develops actionable insights, dashboards, data visualizations, and self-service reporting; improves data quality, governance, standardization, and efficiency. Partners with engineering, AI, operations, and compliance teams to automate workflows, identify risks, evaluate program outcomes, and recommend improvements. Presents findings to senior stakeholders and translates business needs into technical requirements and scalable analytical solutions.