Top AI Research Jobs in Los Angeles, CA

Lead research in language models, personalization, recommendation systems, information retrieval, and generative modeling. Build scalable machine learning prototypes, conduct large-scale distributed experiments, publish findings, mentor junior researchers and interns, and partner with engineering teams to deploy technologies serving Snapchat users. The role requires strong Python and PyTorch expertise, research leadership, publications, and experience applying language models to search, ranking, or personalization.

Conduct foundation-model research for robotic manipulation and high-precision manufacturing. Responsibilities include data curation, simulation, model training, evaluation, rigorous testing on industrial robots, and deployment to production hardware. The intern will work independently on ambiguous AI problems, develop multimodal models, use reinforcement or imitation learning, collaborate cross-functionally, and write maintainable production-quality R&D code.

Lead AI-enabled tools, analytical workflows, and strategic projects for Dynatrace’s Insights team. Partner with leadership, analysts, data science, engineering, and product teams to design, validate, operationalize, and improve AI-driven processes. Interpret complex data, establish governance practices, train teams, document workflows, track adoption and efficiency gains, and research emerging capabilities. The role requires business-focused AI experience, data analysis, executive communication, process design, and the ability to manage ambiguity and iterative innovation.

Graduate research interns will own end-to-end projects developing customer world models and proactive intelligence. Work spans representation learning, foundation models, reinforcement learning, agentic decision systems, and evaluation. Tasks include designing methods, running experiments, publishing findings, and potentially shipping models into production to impact customer-facing products.

Graduate research interns will own end-to-end projects to build customer world models and proactive intelligence: developing representation learning, foundation model, RL/agentic methods, evaluation frameworks, and shipping research into production to improve customer decision-making.

Build evaluation frameworks, datasets, sandbox environments, and metrics for production AI agents. Design experiments to compare models, tools, harnesses, and execution environments; investigate failures; and improve agent reliability, performance, and cost. Contribute to agent infrastructure, reusable knowledge systems, internal tooling, and integrations while collaborating with research, engineering, infrastructure, security, and business teams. This applied research role combines production-quality software engineering with open-ended experimentation.

Label video, image, and metadata content across major social platforms using contextual guidelines. Apply cultural and linguistic judgment, identify edge cases, perform multilingual quality assurance, analyze quality misses, and improve labeling processes. The role requires Hindi and English fluency, careful handling of sensitive content, strong attention to detail, and comfort using AI tools and Google Workspace in a hybrid office environment.

Conduct applied AI research for education by reading papers, reproducing methods, training and evaluating models, building datasets and benchmarks, analyzing educational data, and developing research prototypes. The role also involves documenting experiments, collaborating with researchers and engineers, contributing to publications and open-source work, and assisting with RFP, grant, and research proposal development.

Leads Year Up United’s AI innovation portfolio, maintaining visibility into initiatives, managing intake and governance, running low-cost pilots, and making evidence-based go/kill decisions. The role evaluates emerging AI capabilities, builds organizational AI fluency through communities and Champions, partners cross-functionally, and hands validated pilots to technical teams for scaling. It also includes mentoring students and occasional travel to organizational sites.

Research and develop AI systems that automate scientific research and quantum computer design while helping build and operate fault-tolerant neutral atom quantum computers. The role involves frontier AI research within a highly collaborative team. Prior quantum computing knowledge is not required, but a strong desire to build quantum computers and interest in frontier AI are important.