Top AI & Machine Learning Jobs

The AI Research Scientist will design, develop, and deploy AI/ML models for public safety applications, focusing on cutting-edge research and collaboration with engineering teams.

The Data Scientist II role involves conducting data analyses, managing image processing, optimizing data workflows, and implementing Generative AI techniques within cloud environments.

Lead advanced research in deep learning models for visual categorization, generative AI, and automate data workflows. Collaborate to transition models to production.

Develop scalable AI/ML software solutions, ensure quality and compliance, optimize performance, and collaborate on real-time systems. Manage data pipelines and models with a firm grasp of best practices.

Lead and mentor a high-performing engineering team focusing on AI and ML solutions. Define technical strategy for integrating AI capabilities across products, manage project timelines, and ensure delivery of high-quality features.

The Machine Learning Engineer will apply machine learning techniques to improve advanced MIMO radios' performance, analyze RF datasets, collaborate on ML use cases, and stay updated on ML research.

Lead research and engineering for fine-grained visual recognition and data-centric AI. Develop SOTA models (supervised and self-supervised), build VLM/generative solutions, design agentic data workflows and synthetic-data pipelines, and drive research-to-production transitions while mentoring teams and collaborating on model distillation for edge deployment.

Lead the AI strategy and vision for Avigilon products, defining and architecting AI-enabled user experiences, and collaborating across teams to drive consistency and performance in AI capabilities.

The Data Scientist will collect and preprocess data, develop statistical models, implement machine learning algorithms, and collaborate with teams to deliver analytical solutions and communicate findings effectively.

The role involves modernizing the data ecosystem for AI/ML tools by defining data architecture, governance, quality standards, and strategies for secure data access, as well as overseeing training and mentoring teams on best practices.