Top Machine Learning Jobs in San Luis Obispo, CA

Develop ML models and pipelines for autonomous tractor initiative, improving system safety and performance in production environments.

As a Sales Executive, you will hunt for new clients, manage enterprise accounts, and create revenue streams while collaborating with internal teams.

As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.

As an ML Engineer, you will build and deploy machine learning models for autonomous robots, collaborating with ML scientists and engineers to enhance AI capabilities.

The Senior Product Manager will lead the strategy and execution of AI-driven features in a SaaS platform, ensuring high-quality user experiences and data-driven decisions while mentoring junior team members.

Develop and optimize large-scale ML models and training pipelines for automating PCB layouts, collaborating on data pipeline design and novel architectures.

As a Senior ML Ops Engineer, you'll build ML infrastructure, implement automated deployment, optimize model serving, and ensure production performance.

Design and deploy machine learning models for autonomous vehicles, focusing on data tagging, dataset curation, and model validation.

The Engineering Manager - Data & ML will redesign and scale Underdog's data and ML platform, establishing best practices, leading a team, and collaborating with cross-functional teams to optimize data systems.

Lead the development of AI agents and infrastructure, collaborate with stakeholders, design APIs and SDKs, and advocate for best practices.

The Solutions Architect for AI/ML at Snowflake will advise clients on best practices for Data Science workloads, design solutions, and build ML pipelines. Responsibilities also include working hands-on with SQL and Python, maintaining technical expertise and collaborating with teams to improve products.

The Senior Machine Learning Engineer will develop scalable ML systems, lead a team, mentor juniors, and work cross-functionally to enhance customer experience through machine learning solutions.

The Practice Manager leads Data Science, AI, and ML projects, managing teams, fostering growth, and ensuring customer success through technical guidance and strategic business planning.

Lead the design, development, and implementation of machine learning products, creating data pipelines and maintaining production stability.

Join Equilibrium Energy as a Staff/Sr Staff Software Engineer to enhance the Science Platform for ML model deployment, requiring strong Python and ML expertise.

The Machine Learning Engineer will fine-tune and deploy Large Language Models, optimize ML models, maintain data pipelines, and collaborate on AI features.

The Staff Machine Learning Engineer will design and deploy ML models, build real-time model APIs, and collaborate across teams to drive user growth and revenue through AI/ML solutions.

Contribute to AI/ML capabilities for personalization in Yahoo Mail, develop tools for data processing, and collaborate with cross-functional teams.

The ML Platform Engineer will develop and scale ML infrastructure, automate workflows, and collaborate with data scientists to deploy models efficiently in production.

Design and implement machine learning algorithms for robotic manipulation, focusing on RL and IL. Collaborate across disciplines to ensure integration and successful deployment.

As a Machine Learning Engineer, you'll build scalable ML systems for personalized recommendations, collaborating with data scientists and cross-functional partners.

The role involves building scalable AI features, optimizing machine-learning pipelines, evaluating LLM outputs, and ensuring compliance with data governance standards.

Develop and deploy ML models for personalization and optimization, analyze performance, and collaborate with product teams to enhance user experiences.

The role involves developing and optimizing intelligent agents using large language models, collaborating with the team, and solving technical problems.

Design, develop, and deploy machine learning systems, improve ML infrastructure, and partner with stakeholders to enhance data science workflows.