Top Machine Learning Jobs in Orlando, FL

The AI Engineer will design and build production systems, focusing on LLM integration, system reliability, evaluation frameworks, and high-quality product features while maintaining strong engineering practices.

The role involves developing and improving TTS/Voice Generation models, training, fine-tuning, evaluating speech models, and collaborating across research, engineering, and product teams in a fast-paced startup environment.

As a Software Engineering Intern, you will contribute to building features for user-facing products, APIs, and scalable services for machine learning. Responsibilities include crafting code, designing data pipelines, implementing tools, and enhancing technical documentation.

The Business Development Director will lead strategic engagement with Google Cloud, focusing on partnership growth, go-to-market strategies, and revenue generation through cloud solutions.

The Business Development Director will lead strategic engagement with Oracle Cloud Infrastructure, focusing on alliance strategies, go-to-market execution, and revenue growth through innovative cloud solutions.

The role involves creating insightful technical content on AI and GPU topics, engaging with researchers, and validating technical claims. Requires independent research and technical writing skills.

Lead the Prediction and Planning teams at Motional, overseeing machine learning initiatives, establishing technical roadmaps, and mentoring engineers. Publish research and collaborate across teams to integrate developments into vehicle platforms.

Lead strategy, build and scale a global partnerships ecosystem (ERP, fintech, banks, ISVs, VARs, SIs) to drive partner-sourced pipeline, revenue, and platform expansion. Own partner negotiations, commercial models, cross-functional integration, and team development while leveraging AI/data insights and ensuring compliance.

Sell ExtraHops network security analytics to U.S. Federal/Department of War customers by developing territory accounts, engaging senior federal decision-makers, driving new business and renewals, coordinating with resellers and engineers, delivering tailored demos, and executing strategic account plans to meet revenue targets.

Serve as primary post-sales strategic partner for largest enterprise clients, driving AI Agent adoption, ROI, optimization, governance, renewals, and cross-functional advocacy to turn customers into champions.

Lead and manage the engineering team to design, build, deploy, and maintain cloud-based social media/influencer marketing and AI platforms. Oversee software architecture, large-scale data pipelines, image/video processing, and AI/ML solutions. Coordinate cross-functional work, hiring, project planning, Agile delivery, and continuous system improvement.

Lead and grow a team of 5-7 enterprise sales reps in the Bay Area, recruit and develop sales leaders, build business plans, drive new-logo acquisition and account expansion, manage the sales cycle from lead generation to close, develop GTM strategy, and report to the VP of Sales. Role supports ambitious quarterly and annual goals and may require occasional work outside standard hours.

Drive sales and revenue growth within a named territory by managing the full sales cycle, building executive relationships, meeting revenue targets, collaborating with solutions and delivery teams, negotiating contracts, managing accounts, representing Cardamom at industry events, and supporting marketing and product improvement efforts.

Own marketing measurement, operations, and analytics: build and QA campaigns, set goals, measure performance and attribution, manage data infrastructure and segmentation, optimize lead scoring/routing, apply AI/ML for automation and predictive insights, and document scalable processes to drive demand generation and cross-team alignment.

Lead customer-facing data science engagements: manage data acquisition and cleaning, build and evaluate scalable models, drive model best practices and ethics, write extensible code, research industry trends, define solution strategy, and collaborate across engineering and product teams to deliver AI/cloud solutions.

Lead statistical design and analysis for Phase I-IV clinical studies, develop protocols and SAPs, perform efficacy and exploratory analyses, run simulations, apply advanced methods (mixed models, longitudinal), oversee datasets (ADaM/CDISC), use R and SAS for analyses, and provide strategic statistical guidance to cross-functional teams supporting regulatory submissions.

The Director of Data Sciences leads a team in strategic initiatives, drives machine learning and AI solutions, and manages resources to enhance data-driven decision making across the business unit.

Drive supply chain efficiency through data analytics, KPI development, process redesign, ERP/WMS/automation selection and implementation, Lean Six Sigma continuous improvement, AI-driven forecasting, cross-functional project leadership, audits, and change management with suppliers and 3PLs.

Lead end-to-end customer implementation projects for an AI-driven scientific platform. Create project plans, manage timelines, risks, and resources, coordinate cross-functional teams, act as primary customer PM contact, and ensure on-time, on-budget delivery and customer satisfaction.

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

Lead product strategy and execution for a B2B SaaS marketing analytics platform. Translate research into requirements, own the backlog, partner with engineering and GTM, define success metrics, and ship API- and data-heavy features for technical users.

The Senior Director of AI Insights leads AI-driven data insights to enhance decision-making and strategy alignments across the organization, fostering stakeholder engagement and AI literacy.

The role involves cultivating technical relationships with Accenture, supporting solution development, customer deal support, and representing Databricks at events.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.