Top Remote Machine Learning Jobs in Raleigh, NC

The AI/ML Engineering Manager leads a team of ML engineers and architects, managing hiring, performance, and customer engagements while providing technical guidance in ML operations and architecture.

As a Fraud ML Engineer, you will ensure authentic human data is processed by building detection systems using adversarial ML techniques and managing backend systems.

As an AI/ML Presales Solutions Architect, you will engage with clients to identify problems, create feasible AI solutions, and partner with sales to ensure project delivery aligns with capabilities. You will leverage your extensive experience in AI and cloud platforms to shape presentations and technical discussions with clients.

The AI Strategist leads AI initiative ideation and planning, collaborates across departments to define use cases, and ensures alignment with strategic and regulatory goals.

As an AI/ML Architect, you will define scalable AI systems architecture, mentor teams, guide clients, and ensure ethical AI practices.

Build and maintain MLOps pipelines, deploy and monitor ML models as scalable services, transform and integrate healthcare data (FHIR/HL7), ensure reproducible feature management, and uphold HIPAA/HITRUST security for production ML in clinical systems.

As a Senior ML Engineer, you'll own ML-driven features from modeling to deployment, drive innovation in generative AI, and mentor junior members.

As a Senior AI Engineer, you will develop and optimize Generative AI applications, manage AI pipelines, collaborate with clients, and ensure production delivery with MLOps best practices.

Work on vision-language model development and infrastructure: improve VLM understanding, OCR and function-calling workflows, fine-tuning recipes, evaluation and robustness, and optimize training/serving pipelines. Own end-to-end projects from experiments to production-ready code, collaborating on scale, cost, and reliability.

The CX Outbound Solution Engineer designs and deploys outbound contact center solutions, focusing on operational efficiency and customer engagement. They work with sales to align solutions with customer needs and lead pilot implementations with a strong grasp of outbound regulations and technologies.

Lead Android-first development of growth experiments and features, using AI-native workflows to design, implement, instrument, and analyze A/B tests. Ship performant, reliable Android experiences at scale, mentor teammates, participate in on-call, and codify AI-native engineering playbooks for broader adoption.

Build and ship ML-powered SaaS features end-to-end: design data/feature pipelines, develop and deploy models (forecasting, ranking, recommendations, optimization), define evaluation/A-B testing, monitor drift, integrate models into product workflows, influence roadmap, mentor teammates, and support MLOps and cloud data infrastructure to ensure measurable product impact.

Design and improve ML training frameworks, modularize experiments, adapt fire-hazard detection algorithms for varied electrical environments, build scalable pipelines for high-frequency sensor data, and develop dashboards and tooling to monitor model performance and drift while collaborating across product, data science, and fire safety teams.

Develop and deploy production-grade AI/ML models, design end-to-end pipelines, and ensure compliance while collaborating with stakeholders in a federal environment.

As a Machine Learning Engineer, you will design, build, and deploy ML models for underwriting at Stripe Capital, ensuring performance while collaborating with multiple teams.

The Machine Learning Systems Engineer will optimize ML training systems, improve data pipelines, and develop GPU kernels to enhance scalability and performance.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

The role involves designing, training, and evaluating ML models, optimizing performance, and collaborating with teams to implement AI technologies in products.

Develop and deploy advanced ML solutions for OCR and NLP, optimizing models for performance and integrating them into production workflows while collaborating with cross-functional teams.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

Lead and grow the AI/ML Engineering team, manage core AI/ML platform development, ensure production-grade service operational excellence, and develop product strategies.

Lead development and execution of MVB's AI and automation strategy, including Generative and Agentic AI and RPA. Partner with business and technical stakeholders to design, pilot, and scale solutions, establish governance and best practices, monitor performance metrics, manage vendors, and deliver training to ensure ethical, secure, and effective AI deployments.

The Sr. Director, Marketing Planning & Transformation will lead integrated marketing strategies and AI execution, optimize marketing operations, and drive team success in a fast-paced environment.

Design, develop, and deploy production ML models and scalable pipelines. Collaborate with cross-functional teams, optimize model performance, implement MLOps deployments, monitor model health, and document experiments and metrics.

Lead design and implementation of agentic AI/ML solutions and modern software (APIs, microservices, CI/CD, streaming) on cloud platforms. Bridge legacy IBMi AS400 systems with modern AI, train and fine-tune models, oversee lifecycle, mentor engineers, set technical direction, and improve automation and testing across claims adjudication systems.