Foundational Model Engineer — Multimodal & Agentic Medical AI Systems

SAIGroup is focused on developing cutting-edge medical AI systems, and they are seeking a Foundational Model Engineer to build the technical infrastructure for their multimodal AI models. The role involves architecting large-scale training pipelines, optimizing performance for 3D medical data, and collaborating with researchers to advance clinical-grade AI systems.

Responsibilities:

• Architect and maintain large-scale training pipelines for multimodal foundation models (3D volumes + text)
• Implement distributed training using data parallelism, tensor parallelism, pipeline parallelism, and FSDP/ZeRO strategies
• Optimize training performance across A100/H100 clusters, including kernel-level optimizations and memory efficiency tuning
• Build scalable ingestion, preprocessing, and storage systems for 3D medical volumes, DICOM series, voxel grids, and text datasets
• Create multimodal data loaders and augmentation pipelines for high-throughput training
• Work on dataset versioning, weak-label pipelines, and automatic metadata extraction
• Build and optimize inference runtimes for 3D-aware models and LLM-based medical agents
• Develop robust APIs and service layers for clinical workflows (retrieval, reporting, case summarization, multi-step agent chains)
• Implement caching, quantization, batching, vector search, and agent orchestration
• Develop tools for researchers: experiment launchers, logging/visualization dashboards, model evaluation notebooks, and reproducibility tooling
• Partner closely with scientists on rapid model iteration, ablations, and experimental design
• Participate in internal 'ML performance tiger teams' to squeeze maximum throughput from models and data pipelines