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

Lead and scale the sourcing function, develop data-driven strategies, and build a high-performing team while meeting diverse talent needs.

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 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.

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.