Top Remote Machine Learning Jobs in Raleigh, NC

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 procurement strategy and supplier selection for inbound products and services. Manage RFIs/RFPs, negotiate contracts with Legal, administer Coupa purchase orders, reconcile invoices, and apply AI/ML and reporting tools to optimize procurement processes while collaborating with cross-functional senior stakeholders.

Lead technical execution across backend, frontend, mobile, AI, and data. Translate ambiguous product goals into technical plans, unblock hard engineering problems, coordinate cross-domain work, raise engineering quality, define hiring needs and technical evaluation standards, and build internal AI workflows to foster an AI-native engineering culture.

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.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

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.

Lead development and roadmap of AI-powered products for internal teams and advertisers, translating AI concepts into product requirements, partnering with engineering, data science, sales, and ad ops to launch targeting, creative, analytics, and automation solutions.

Research, deploy, and maintain AI models and automation solutions; integrate systems and APIs; manage AWS infrastructure and PostgreSQL databases; build BI dashboards; monitor and optimize AI systems and provide technical support.

The AI Executive Partner will advise C-Suite executives on leveraging AI to drive business outcomes, manage member relationships, and deliver innovative AI solutions.

Lead and deliver complex, cross-team AI product programs, drive agent-first SDLC adoption, ensure integration and operational readiness of AI workflows, define metrics, communicate risks and status, and standardize TPM best practices across Product and Engineering.

Design, develop, and implement secure, scalable cloud systems (Azure/AWS); perform system engineering, risk assessments, and NIST-compliant ATO/ATD processes; manage secure data ingestion and orchestration; integrate AI/ML concepts; secure communications and network infrastructure; present technical findings to senior USG leadership and provide hands-on support.

Lead end-to-end healthcare fraud investigations for Clover's Medicare Advantage business. Own caseloads, gather and analyze claims and medical evidence, coordinate legal support, prepare defensible referral packages, leverage AI and analytics tools, and collaborate cross-functionally to drive recoveries and provider actions.

Lead architecture and implementation of high-performance software to deploy and operate AI/ML models for network threat detection. Build autonomous AI agent workflows, production ML pipelines, and services; drive cross-team engineering standards, mentor engineers, and represent technical direction to leadership.

Develop and maintain scalable, performant web applications and data pipelines. Collaborate with engineering managers, product, and stakeholders using Agile practices. Emphasize observability, testing, CI/CD, security, and incremental releases to deliver robust production systems.