Top Remote Machine Learning Jobs in San Luis Obispo, CA

The role involves designing and building AI components for a platform, specifically focusing on NLP and ML for catalog mapping and anomaly detection, along with feedback mechanisms.

The role involves developing and deploying machine learning solutions, optimizing models, and collaborating with cross-functional teams. Responsibilities include cloud architecture, algorithm engineering, and infrastructure best practices.

The Applied Scientist AI/ML Intern will work on building data pipelines, finetuning LLMs, and deploying AI models in a collaborative team environment.

Lead a team of machine learning data scientists, overseeing model development for fraud detection and risk assessment, and ensuring quality throughout the model lifecycle while mentoring the team.

As an MLOps/ML Platform Engineer, you will build and manage ML systems, optimize workloads, and ensure production model reliability while collaborating across teams.

As a Senior Machine Learning Engineer, you will develop and deploy ML models for health data analysis, collaborating with cross-functional teams to ensure reliability and regulatory compliance.

Design and implement ML infrastructure for scalable deployment, collaborate across teams, optimize ML systems, and enhance tooling.

As an Applied Machine Learning Platform Engineer, you'll design and maintain training infrastructure, manage distributed pipelines, and optimize data workflows for machine learning models.

As a Senior Machine Learning Engineer, you'll design and develop infrastructure and models for AI-powered applications while collaborating with teams to enhance product functionality.

The VP of Research, Machine Learning will set research direction for AI systems, focusing on context representation, reasoning, and evaluation frameworks while ensuring product safety and alignment.

Responsible for building and maintaining end-to-end ML pipelines and deploying production-grade ML systems. Tasks include model fine-tuning, evaluation, and collaboration with engineering teams.

Lead computer vision projects and develop ML models for power grid analysis while mentoring engineers and ensuring software quality standards.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

The AI Director will oversee ML bot development and implementation, identify AI opportunities, educate teams, and prototype AI solutions.