Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Design and build optimized solutions for AI workloads on supercomputing infrastructure, troubleshoot large-scale GPU deployments, and enhance system usability and reliability.

As a Distributed Systems Engineer, you will build data systems for ultra-long context inference, focusing on performance, availability, and troubleshooting across various environments.

As a Kernel Engineer, design and maintain high-performance kernels, optimize training and inference performance, and evaluate compute kernels for alternative hardware.

As a research engineer, you'll train and evaluate large AI models, optimize inference throughput, and build data pipelines to enhance research and production workflows.

Develop and maintain data pipelines for gathering post-training datasets, ensuring data quality and efficiency in processing and generation.

Contribute to building safe AGI through automating research and code generation, enhancing models, and aligning with team values.

As a Software Engineer, build and maintain product surfaces that integrate with AI technology, collaborating with design and research teams.

As a Technical Sourcer, you'll source and engage technical talent for AI roles, collaborating with teams to build candidate pipelines and optimize sourcing strategies.

As a Technical Recruiter at Magic, you'll collaborate with executives to identify and hire top technical talent in AI and engineering, managing the candidate journey and providing market insights.

Build and optimize secure, scalable distributed systems for AI research, focusing on sandboxed execution, performance, and resource management.