Sciforium

Engineer

• Design, implement, and optimize custom GPU kernels using C++, PTX, CUDA, ROCm, Triton, and/or JAX Pallas
• Profile and optimize end-to-end performance of ML operations, focusing on large-scale LLM training and inference
• Integrate low-level GPU kernels into PyTorch, JAX, and custom internal runtimes
• Develop performance models, identify bottlenecks, and deliver kernel-level improvements that accelerate AI workloads
• Collaborate with ML researchers, distributed systems engineers, and model-serving teams to optimize compute performance across the stack
• Work with NVIDIA and AMD hardware vendors and stay current on GPU architecture and compiler/toolchain improvements
• Contribute to tooling, documentation, benchmarking suites, and testing frameworks for correctness and reproducibility
• Build full-stack infrastructure powering frontier AI models and real-time applications at Sciforium, an AI infrastructure company

GPU Kernel Engineer

Data Engineer

• Own the end-to-end creation of pre-training datasets for LLMs
• Define the mix of web data, code, books, and technical papers to optimize downstream model performance
• Design and implement pipelines for data cleaning, exact and fuzzy deduplication, and signal extraction from petabytes of raw unstructured data
• Lead development of post-training datasets, including SFT instructions, multi-turn dialogues, and RLHF/DPO preference modeling data
• Drive acquisition and processing of vision and video data
• Address multimodal alignment, video compression, and temporal data consistency
• Develop high-throughput Python data-processing scripts using multiprocessing and multithreading
• Conduct statistical analysis of training corpora to identify biases, knowledge gaps, and quality regressions
• Design synthetic-data generation pipelines to augment gaps in natural datasets

GPU Kernel Engineer

Foundation Model Data Engineer