As an AI Science Intern, you will work alongside AI Scientists and Machine Learning Engineers to build, train, and evaluate machine learning and deep learning models on real-world datasets. Responsibilities include data gathering and ETL, feature engineering, model development using techniques like classification, regression, clustering, and neural networks, and designing experiments to measure impact.… You'll explore current AI/ML research including generative AI and LLMs, develop proofs-of-concept, and partner with cross-functional teams to translate insights into product features. The role requires strong Python and SQL skills, familiarity with core ML techniques and their mathematical foundations, and the ability to communicate findings to both technical and non-technical audiences.
regression jobs in San Diego
This role blends data science expertise with hands-on automation engineering to design and deploy intelligent process automation solutions across business units. You will build, train, and validate both classical ML and LLM-based models — including fine-tuning and RAG — and architect end-to-end systems connecting CRM, APIs, and AI services on AWS infrastructure.… Responsibilities span solution design, CI/CD implementation with Jenkins, Python development, agentic AI capabilities, and business translation, with emphasis on measurable business outcomes and compliance.
Staff Analytics Engineer responsible for designing and operationalizing agentic AI workflows, ML models, and Databricks applications at scale. Will build multi-step agent pipelines combining rules, ML models, and reasoning to solve complex business problems, then productionize them with monitoring, drift detection, and retraining strategies.… Requires 5+ years of hands-on ML engineering or data science with production system ownership, deep Python proficiency, strong traditional ML foundations, and proven Databricks expertise including notebook apps, dashboards, and ML pipelines. Will serve as technical authority, mentor peers, and influence architectural decisions across teams.