01Overview
About the Role
You will be one of the earliest engineering hires responsible for building the technical backbone that powers our 3D-volume foundation model and the agentic medical AI systems built on top of it.
This role blends ML systems engineering, high-performance computing, and foundation-model infrastructure, enabling our research scientists to train and deploy cutting-edge multimodal models at scale.
You will design the pipelines, tooling, distributed systems, and evaluation frameworks that make world-class research possibleand usable in clinical settings.
If youre the kind of engineer who loves training clusters, PyTorch internals, scalable data loaders, CUDA kernels, model parallelism, and agentic inference systems, this is your role.
What You Will Work On
Model Training Infrastructure & Systems
- 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.
Data & Multimodal Engineering
- 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.
Model Serving & Agent Runtime
- 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.
Tooling & Collaboration
- Develop tools for researchers: experiment launchers, logging/visualization dashboards, model evaluation note .
