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Build and productionize distributed pre-training stacks (PyTorch/JAX) at multi-node GPU scale, implement new optimizers and high-performance CUDA/Triton kernels, optimize mixed-precision training and memory/communication, develop experiment infrastructure and monitoring, and mentor junior engineers to accelerate foundational model research.

Intern will build and validate a simulator and profiling framework to model inference on NVIDIA GPUs, develop analytical GPU kernel performance models, profile kernels with Nsight tools, analyze PTX/SASS, identify bottlenecks, and recommend kernel and system-level optimizations for transformer inference on Hopper and Blackwell architectures.

Rapidly collect, curate, and preprocess high-quality NLP datasets. Build and maintain web crawlers, APIs, automated workflows, and scalable data pipelines. Refine and evaluate LLM outputs, ensure data quality and performance, document methodologies, and collaborate with researchers and engineers to deliver data for model training and benchmarking.

Drive research and engineering to develop PAN world models: scalable training on large clusters, video data pipelines and annotation, evaluation benchmarks, visual tokenization, and inference optimization for real-time interaction.

Provide local finance and administrative coordination for IFM Sunnyvale: collect, review, organize, and track invoices, receipts, credit card bills, vendor records, and reimbursements; liaise with HQ Finance; maintain trackers, vendor files, contracts, and simple financial summaries; support banking/platform setup and general office administration.

Conduct and prototype novel reinforcement learning research for foundation models, including massive-scale self-play, end-to-end engineering from data curation to production, contribute to training/inference frameworks, publish and present results, engage with open-source and external collaborators.