Top Machine Learning Jobs in Raleigh, NC

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

Join an R&D team to develop image-processing and computer-vision algorithms for 2D/3D scene segmentation, design experiments to detect long-tail model failures, and explore deep learning models and active learning methods to improve autonomous robotic vision systems.

Lead development of internal Python SDKs and agentic AI workflows, build scalable FastAPI ML services, maintain CI/CD with Jenkins/GitLab Runners, and use Galileo for model evaluation and observability to productionize autonomous systems.

The Staff Engineer will design and manage ML infrastructure while mentoring teams, driving MLOps strategies, and ensuring compliance in healthcare data systems.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.

Lead program delivery for engineering teams at Arine, optimizing internal processes and ensuring timely execution of technical programs while facilitating Agile methodologies.

Design and develop machine learning systems for construction robots, including scalable infrastructure, computer vision models, and data analysis for performance improvement.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

As a Senior Machine Learning Engineer at ZoomInfo, you will enhance core datasets through ML/NLP solutions, manage entity resolution tasks, and drive cross-functional impact by mentoring teams and collaborating on ML KPIs.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

Build and design backend services, AI agents, and machine learning models. Collaborate cross-functionally to deliver AI features and maintain code quality.

The Senior ML Data Scientist will develop machine learning models to detect fraud, manage the data science lifecycle, and collaborate with teams on strategies.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.

Design, research, and deliver LLM and GenAI solutions for healthcare SaaS at scale: architect systems, handle healthcare data, implement PEFT and RAG pipelines, ensure responsible AI, mentor teams, and translate technical outcomes to business impact.

Design and operate production ML systems for large-scale streaming data, ensuring reliability and performance while collaborating with researchers to integrate models into distributed systems.

Design, develop, and deploy machine learning models; analyze large datasets; build scalable data pipelines and distributed computing environments; integrate ML solutions with cross-functional teams; optimize performance and support production ML infrastructure.

Design, build, and productionize scalable AI systems for autonomous detection, RAG-backed investigations, and auto-remediation. Develop LLMs, graph-based reasoning engines, streaming pipelines, evaluation/red-team processes, and collaborate with cross-functional teams to translate attacker behavior into robust detections.

Lead design and delivery of scalable AI systems for autonomous threat detection and response. Build and productionize LLMs, graph reasoning engines, streaming feature pipelines, and orchestration frameworks. Own evaluation, reliability, and cross-functional collaboration while mentoring engineers and experimenting with RAG, tool-calling agents, and multi-modal models.

Lead assessment, redesign, and hands-on implementation of data pipelines, ML workflows, and platform infrastructure. Produce prioritized system designs, harden production pipelines, improve observability, reliability, and data quality, and mentor the data team.

The Machine Learning Engineer will design and build systems that improve the AI shopping agent by using real user feedback, focusing on feedback loops and metrics to enhance user interactions and agent performance.

Design, build, and deploy scalable ML models and pipelines to enhance a talent marketplace and GenAI solutions. Lead experimentation (A/B tests, statistical validation), improve production ML performance, ensure code and data quality, work with cross-functional teams, mentor junior engineers, and propose innovative AI-driven solutions aligned to business goals.

Lead technical roadmap and architecture for ML data infrastructure supporting petabyte-scale pipelines. Manage and mentor engineering team, design ingestion/transformation/serving systems, ensure data quality, lineage, governance, and collaborate with ML and infrastructure teams to deliver production-grade ML data pipelines and feature stores.

Own end-to-end client relationships for a book of B2B SaaS customers: drive adoption, engagement, retention, and expansion of AI-powered contract drafting tools. Lead onboarding, training, large-scale rollouts, and translate customer feedback into product improvements while designing scalable CS processes and tracking health metrics to demonstrate value.

Develop scalable web applications using React and Next.js, implement backend solutions in Python, maintain DevOps practices, and collaborate with AI/ML teams to deploy applications.

The role involves preparing traffic data, developing and deploying machine learning models on Google Vertex AI, and collaborating with teams to improve project workflows.