Top Machine Learning Jobs in Tallahassee, FL

Lead and scale engineering teams building Protect, a real-time multi-signal fraud detection platform. Set technical strategy, develop managers and senior engineers, deliver distributed systems and APIs, improve architecture, observability, reliability, and developer productivity while partnering with Product, Research, Security, and exec stakeholders.

Design, build, and deploy AI-driven documentation and localization systems (TMS/CMS), improve translation memory and automation, support DITA/XML workflows, develop Python scripts for publishing, and implement generative AI/NLP solutions within a regulated life-sciences environment.

Lead client-facing Data & Analytics engagements across pre-sales, architecture, and delivery. Define strategy, roadmaps, solution designs, and SOWs for modern data platforms, pipelines, governance, analytics, and AI enablement. Drive practice development, create reusable IP, mentor teams, and ensure quality delivery and strong business outcomes.

As a Senior Machine Learning Engineer, you'll design and develop workflows utilizing vision-language models, manage data pipelines, and collaborate with multiple teams to enhance model accuracy and efficiency.

As a Staff Software Engineer, you will lead the development of computer vision systems for sidewalk inspection, overseeing model lifecycle, system architecture, and mentoring while improving deployment practices.

As a Principal Software Engineer, you'll architect and code backend services and ML models, integrate them with Salesforce, and mentor junior engineers.

The Staff Product Manager will define product strategy, lead the product lifecycle, collaborate across teams, and drive innovation in claims processing software.

The Director of System Safety oversees safety functions for autonomous vehicles, managing safety cases, deployment plans, and regulatory relationships while leading a multi-disciplinary safety team.

The Global Process Owner for S&OP leads the design and continuous improvement of the S&OP process, ensures collaboration across functions, and drives digital transformation and automation within the organization.

The VP of Data and AI will define and execute data and AI strategies, lead technical teams, oversee data platform infrastructure, and drive measurable business outcomes.

The Senior Technical Product Manager will lead the roadmap for Aledade's AI Data Platform, collaborating with various teams to enhance AI capabilities and improve healthcare outcomes for patients.

Lead a team of analysts to advance fraud and safety operations through analytics, ensuring robust data products and compliance while empowering stakeholders with actionable insights.

The Software Engineering Manager will lead a team in creating tools for Cengage Learning Platforms, focusing on AI innovation, team growth, agile delivery, and collaboration.

The Director of Software Engineering will lead teams in creating innovative education tech solutions, strategic planning, and optimizing software delivery processes.

Lead actuarial strategy and execution for Medicaid, Medicare Cost, and Medicare Supplement products. Oversee pricing, rate filings, forecasting, financial analysis, regulatory interactions, and team leadership. Partner cross-functionally to influence product design, manage regulatory change, modernize models with predictive analytics and cloud tools, and communicate actuarial insights to executives.

Own a product area of the Content Authoring Platform end-to-end: set vision and roadmap, run continuous discovery, define and measure outcomes, partner with engineering and design to deliver features, evaluate and apply AI capabilities prudently, mentor peers, and communicate strategy and trade-offs to stakeholders.

Build and maintain production AI/ML models and pipelines for an NDR platform; develop scalable, secure backend services; optimize detection algorithms and reduce false positives; integrate ML features into product architecture; mentor engineers; participate in code reviews, technical strategy, and documentation; operate cloud-native, containerized services and manage infrastructure as code.

Lead discovery, definition, and delivery of B2B SaaS product features. Own customer research, data-driven prioritization, UX collaboration, KPI tracking, and cross-functional execution with engineering, marketing, and customer success.

Lead enterprise design and deployment of a global operating model for bovine IVF production. Drive standardization, SPC, automation, AI/analytics, Lean/Six Sigma, quality systems, and continuous improvement to scale consistent, high-throughput production across labs and field operations.

Lead pre-sales for Workday Financials-focused AI SaaS: run discovery, demos, technical workshops, respond to RFIs/RFPs, collaborate with Product/Engineering, support sales expansion and market initiatives.

Own a portfolio of large manufacturing accounts, drive net-new pipeline and full-cycle enterprise deals, build C-suite relationships, translate AI/vision capabilities into ROI, execute land-and-expand strategies, and travel 40-50% for on-site engagement and executive reviews.

Lead the commercial engine: build partnership function, close enterprise platform partnerships, own full marketing (positioning, content, paid, website), design AI-leveraged GTM stack, translate product-market fit, run pipeline and commercial reporting, and hire a team when metrics justify growth.

Lead development and integration of predictive pricing models for home insurance, modernize state rating plans, support rate filings, monitor performance and recommend pricing actions, analyze competitor strategies, develop pricing for expansion, build internal pricing analytics software, and coach/mentor analysts.

Lead and coordinate end-to-end fraud risk engagements, structure analytical solutions, guide analytics teams, communicate with clients and senior stakeholders, manage timelines, build fraud rules/governance, and identify additional client opportunities.

Lead design and deployment of reinforcement learning and sequential decision models for collections and recovery. Build scalable ML pipelines (Databricks/Spark), run experimentation and offline policy evaluation, collaborate with engineering/MLOps to productionize models, and mentor junior data scientists.