Top Remote Machine Learning Jobs in Raleigh, NC

Design and develop AI capabilities and scalable microservices. Collaborate to integrate AI models and ensure performance in cloud environments.

Seeking a Senior Machine Learning Engineer to design and implement ML systems, optimize deployment, and collaborate with data teams, focusing on scalability and performance.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

The Director of Data Engineering, ML & AI leads data strategy and AI capabilities, managing a team to enhance data infrastructure, machine learning systems, and sustainability analytics while driving company-wide AI initiatives.

The Machine Learning Engineer will develop and maintain scalable ML systems, collaborate across teams, and innovate solutions for real-time applications to enhance customer experiences.

Design, build, and scale MLOps infrastructure and CI/CD/continuous training pipelines. Deploy and monitor ML models as microservices or batch jobs, manage cloud GPU/CPU resources with IaC, enable observability for model drift and latency, and collaborate with data scientists, engineers, and product teams to ensure reproducibility, compliance, and production reliability.

Design, implement, and scale web and data pipeline solutions focused on generative AI and LLMs. Improve AI/ML infrastructure for model development, training, deployment, and prompt engineering. Partner with product and stakeholders to prioritize applied AI use cases, ensure PHI security and regulatory compliance, mentor junior engineers, and contribute APIs and integrations to embed generative AI into healthcare applications.

Design, operate, and scale ML data and training pipelines for autonomous driving. Build distributed training, orchestration, CI/CD and infrastructure-as-code; maintain data/metadata stores and tooling to support large-scale model training and evaluation across cloud and cluster environments.

Lead design, training, fine-tuning, evaluation, and production deployment of TTS/voice-generation models. Translate research into reliable, low-latency production systems, define ML best practices, and collaborate across research, engineering, and product to drive execution.

Design and scale machine learning systems and infrastructure, partner with data and applied scientists, build reusable ML pipelines and abstractions, ensure production readiness using robust software engineering practices, and prototype new technologies to improve customer experience and automate business processes.

Lead development and optimization of the compiler stack for ML systems, collaborate across teams, integrate and deploy products, map ML operations to hardware, and drive compiler infrastructure innovation and performance debugging.

Build and deploy production ML models and end-to-end pipelines to detect suspicious behavior; design evaluation, monitoring, and drift detection; integrate intelligence into product workflows; improve data quality and accessibility; and contribute to operational rigor, runbooks, and safe rollout practices in a security-sensitive environment.

The Solutions Engineer will drive the adoption of AI-powered solutions in the electric utility sector, providing technical consulting and customer enablement. Responsibilities include gathering requirements, architecting solutions, leading RFI/RFP processes, and delivering training. Extensive travel is required for client engagement.

As a Software Engineer in ML Infrastructure, you will develop scalable data pipelines, enhance data discovery, and collaborate with teams on data organization and ML features.

Develop AI-powered solutions for personalized content and marketing. Collaborate with teams to optimize ML models and pipelines at scale, mentoring engineers and driving strategic growth initiatives.

The role involves developing and deploying machine learning models for forecasting within the Camus platform, requiring collaboration and technical depth in ML.

The Senior Databricks AI/ML Engineer will design and deploy AI/ML solutions in the insurance sector, focusing on Databricks architecture, data pipelines, and MLOps workflows, while collaborating with various teams to operationalize models and optimize processes.

The Senior AI/ML Engineer will develop AI systems for enterprise decision-making, focusing on LLMs and real-time data processing. Responsibilities include building detection systems, insight engines, evaluation frameworks, and collaborating with engineers to ensure robust AI product engineering.

As a Senior Staff Machine Learning Engineer, you will lead the development and deployment of machine learning models, ensuring they drive business growth and meet performance requirements. You will collaborate with teams, manage data pipelines, and implement state-of-the-art solutions while continuously improving model performance.

The R&D Engineer will build and refine software solutions for defense use cases, prototype systems, and convert stakeholder feedback into technical requirements. Collaboration with engineering teams and customer-oriented engagements are key responsibilities.

As a Staff Machine Learning Engineer - AI Tech Lead, you will lead the design of AI systems for security operations, focusing on scalable architectures, AI safety, and mentoring. Responsibilities include overseeing technical evaluations, production workflows, LLM fine-tuning, and collaboration with teams to enhance AI capabilities.

The Machine Learning Engineer will develop AI/ML solutions on AWS, optimize data pipelines, and support cloud migration projects while collaborating with stakeholders.

Manage AI/ML projects from Proof of Concept to Minimum Viable Product using Agile methodologies while ensuring stakeholder communication and project alignment.

Design, build, and operate production-grade ML systems for personalization, search, and recommendations. Own end-to-end pipelines from data ingestion and modeling to deployment, monitoring, and iteration, ensuring scalable, reliable, and observable services.

Responsible for designing, building, and operating AI-powered features, scalable APIs, and maintaining integrations within ClickHouse's AI/ML ecosystem.