Top Machine Learning Jobs in Orlando, FL

Lead development of time-series and econometric models (ARIMA, VAR, state-space, WLS, panel data) and Bayesian probabilistic frameworks. Use causal inference and stochastic processes to produce actionable insights, work with large datasets on Databricks, communicate results to stakeholders, and mentor junior team members.

Lead cross-functional technical programs, coordinate launches, develop scalable processes and KPIs, manage risks and intake prioritization, influence product and technical strategy, and drive timely delivery with engineering and business stakeholders.

Senior Business Systems Analyst to define requirements for agent-facing servicing applications, design integrations/APIs with core banking systems, translate needs into user stories and acceptance criteria, improve agent workflows and productivity, and identify AI/ML opportunities for fraud detection, automation, and operational analytics. Supports testing, validation, and releases.

Lead cross-functional technical programs from planning through delivery. Coordinate engineering, architecture, and business teams to launch capabilities, manage risks, prioritize intake, define KPIs and processes, influence technical strategy, and ensure timely delivery of large customer-facing initiatives.

Gather and document functional requirements for agent-facing servicing applications, define integrations/APIs with core banking systems, consult with stakeholders, identify AI/ML opportunities (fraud, KYC, automation), support testing and implementation, and act as SME while managing risks and compliance.

Lead design, implementation, and scaling of enterprise Palantir data solutions. Oversee cross-functional teams to build data pipelines, integrations, governance, and advanced analytics (ML/AI). Manage project delivery, stakeholder engagement, performance optimization, and mentor senior engineers and analysts while ensuring security and regulatory compliance.

Design, build, and deploy conversational agents using Microsoft Copilot Studio. Gather stakeholder requirements, implement NLU/NLP, integrate with enterprise systems, test and refine agents, and provide documentation and user support for HR, policy retrieval, and procure-to-pay automations.

Design and implement conversational AI solutions using NLP, ML, and LLMs. Build ETL pipelines and integrate relational and NoSQL databases. Develop Voice AI interactions on Vertex AI, work with Nuance IVR and Microsoft platforms, and write production-grade Python code.

Lead digital transformation engagements by developing strategy, advising executives, and delivering end-to-end projects. Drive adoption of cloud, AI, data analytics, and emerging technologies while managing multidisciplinary teams, ensuring compliance, and measuring outcomes through KPIs.

Design, build, and deploy conversational AI solutions on the Kore.AI platform. Create and configure chatbots (intents, entities, dialog flows), integrate with back-end systems and REST APIs, and develop responsive user interfaces using ReactJS alongside UI/UX designers.

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

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.

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.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

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.

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.

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.

The Product Marketing Manager will develop go-to-market strategies, create compelling messaging, perform market research, and collaborate with product and sales teams to position Oscilar's AI risk platform effectively.

Provide legal support for commercialization and product development of an AI-powered ad tech platform: draft and negotiate commercial agreements, manage IP portfolio and complex IP litigation, advise on AI, data, and privacy matters, counsel product teams on compliance and IP protection, and develop automated legal workflows.

Lead Lean transformation initiatives and AI coordination to enhance process excellence across multiple business functions, coaching teams and ensuring integration with operational standards.

Lead enterprise architecture and define future-state cloud, data, and integration platforms. Guide DevOps, data, integration, and infrastructure teams; design scalable secure cloud and on-prem solutions across AWS/Azure/Nutanix; drive IaC, CI/CD, automation, monitoring, cost optimization, and security; advise senior leadership and mentor engineering teams.

Two research fellows will receive a stipend and compute credits to pursue projects in legal AI and automated negotiation. Fellows get expert access, private contract data, and are expected to produce a publishable paper, benchmark, or open-source artifact. Research topics include reward modeling under expert disagreement, negotiation simulation, minimal-diff editing benchmarks, uncertainty/hand-off strategies, or related legal AI problems.

Lead and execute independent research programs on NLP and LLM safety, design and run experiments, secure funding, publish and present results, lead cross-functional teams, and develop evaluation tools and challenges to advance digital safety.

Independently validate and monitor machine learning models for credit underwriting, credit risk, and fraud detection. Identify model weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Support and maintain the Model Risk Management framework and coordinate with Internal Audit, Internal Controls, and Compliance on audit and regulatory requests.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.