Top Machine Learning Jobs in Greensboro, NC

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

The Forward Deployed ML Engineer will integrate AI/ML models into clinical software, assist in model fine-tuning, and engage with clients to ensure success in healthcare deployments.

The position involves driving AI/ML product development for personalized experiences, partnering across teams, and launching impactful products.

As a Machine Learning Engineer at Shipwell, you'll build and scale AI-powered logistics solutions, maintaining data pipelines and ML infrastructure across the organization.

Work on Wayfinding projects using LLMs and agentic workflows, collaborate with engineering/product/research teams, receive mentorship, and gain hands-on experience with model evaluation, prompt engineering, and shipping code in a real engineering environment.

Design and build ML infrastructure components for autonomous vehicles, ensuring scalability and robustness for high-volume data processing and model training.

The AI/Machine Learning Engineer will develop systems for automating health benefits decisions, integrating large language models with rule-driven patterns, and ensuring compliance with regulations.

As the Director of AI & Machine Learning, you will lead data projects, enhance corporate performance, and analyze complex data to drive business improvements and client engagements.

As a Senior Vice President at AlixPartners, you will lead complex projects focusing on performance improvement, data analysis, and machine learning applications to enhance business outcomes.

Senior ML Engineer on the Vision team responsible for framing ML problems, improving data acquisition and training pipelines, designing experiments, addressing model drift, building monitoring/visualization tools, collaborating across teams, and reviewing ML engineers' code to deploy robust models.

The role involves building ML infrastructure, creating data pipelines, ensuring data quality, and collaborating with data scientists to optimize models.

Own productionalization of ML prototypes: deploy, monitor, and maintain scalable, reliable ML services for campaign optimization. Improve model quality, latency, observability, testing loops, data freshness, and rollout safety while sharing on-call responsibility.

The Senior Software Engineer II at Aledade will develop scalable AI/ML solutions, mentor junior engineers, and collaborate with cross-functional teams in the healthcare industry.

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

Drive delivery excellence and customer success by leading the architecture and implementation of AI/ML solutions while ensuring account growth and client engagement.

As a Research Intern, you will work on AI/ML projects, contributing to the development of the Nebula LLM and applying machine learning techniques to real-world problems.

As a ML Engineer focused on inference, you'll design and optimize deep learning models for real-time communication AI applications, collaborating with cross-functional teams and ensuring production readiness.

Design and maintain scalable machine learning infrastructure, collaborating with data scientists, ensuring performance, security, and compliance of AI systems.

The ML Engineering Intern will contribute to the Nebula LLM development, tackle machine learning challenges, and gain hands-on experience in AI and language technology.

Senior ML research leader who translates state-of-the-art ML pipeline insights into product requirements, reference architectures, benchmarks, and platform roadmaps. Partners cross-functionally and with strategic customers and research institutions to set evaluation standards, reproducibility, and responsible ML practices while providing technical leadership and influencing product and executive audiences.

Lead definition and implementation of the companys ML strategy and infrastructure. Build end-to-end production ML systems (data pipelines, training, evaluation, serving), own data strategy and feedback loops, integrate ML with product/backend, write production-quality code, and recruit/mentor the ML team as it scales.

As an AI/ML Ops Engineer, you will architect and operate ML pipelines, automate CI/CD processes, and collaborate with cross-functional teams to enhance engineering productivity.

The role involves deploying, optimizing, and maintaining ML-driven motion planning and control algorithms for autonomous driving, collaborating across teams to ensure reliability under performance and safety constraints.

Serve as the presales technical expert for ML capabilities: lead discovery, architect ML solutions, run demos/workshops/proofs-of-concept, coordinate cross-functional teams, and inform product direction based on customer feedback.

Design, architect, and deploy generative AI/LLM solutions from prototype to production; work directly with clients to translate business needs into technical plans; write production-quality code integrating LLMs and ML models; collaborate with cloud, DevOps, and frontend teams; support proposals, sales, and evaluation/observability of models.