Scientist / Senior Scientist, Multimodal AI

Distributed Training: Proven experience training and fine-tuning large models at scale using frameworks like PyTorch Distributed, DeepSpeed, or Jax.

Programming Mastery: Expert-level Python skills, with a focus on building production-ready machine learning code and large-scale data management systems.

Scientific Contributions: A track record of technical contributions via high-impact publications (CVPR, ICCV, NeurIPS, etc.) or significant contributions to open-source ML frameworks.

Direct experience with Multimodal Fusion (e.g., aligning image embeddings with transcriptomic or proteomic data).

Proficiency with cloud-native AI tools (AWS/GCP, Kubernetes, Docker) and building automated MLOps workflows.

Experience handling the unique noise and sparsity of biological data.

Model Development: Design, code, and train large-scale foundation models (e.g., Vision Transformers, Multimodal LLMs) that can embed spatial data and integrate multiple modalities.

Hands-on Data Fusion: Implement innovative cross-domain mapping and fusion strategies to synchronize heterogeneous biological datasets.

Scaling & Training: Build and manage high-performance ML pipelines capable of processing petabyte-scale image repositories and multi-omics streams in a cloud environment.

Technical Collaboration: Work directly in the trenches with experimental scientists and software engineers to translate biological complexity into performant code and reliable distributed systems.