Jobs at Biohub

Build and scale frontier AI systems for biology: design model architectures, run large-scale pretraining on GPU clusters, develop RL/reward modeling and multi-agent systems, create evaluation frameworks tied to biological outcomes, and publish/open-source research.

Develop and maintain analysis workflows and visualization tools for spatiotemporal multi-omics datasets; integrate proteomics and metabolomics; apply machine learning approaches; collaborate with experimental teams and contribute to publications and open-source software.

Lead design of genomic data representations and tokenization for biological AI models; define data standards and quality metrics; integrate heterogeneous modalities into training frameworks; evaluate representation impacts on model performance; partner with ML engineers and researchers; lead cross-functional data initiatives; drive data acquisition and generation; mentor senior contributors.

Lead an independent, collaborative immunology research program integrating quantitative and systems approaches with AI-modeling to uncover immune system principles, mentor team members, publish findings, and translate discoveries toward engineered immune-cell therapeutics.

Develop and scale frontier AI systems for biology, including foundation models, pre-training pipelines, RL and multi-agent systems, evaluation frameworks, and community-facing publications and open-source releases to accelerate scientific discovery.

Design, build, and optimize custom light-sheet microscopes and imaging subsystems. Develop modular hardware control software (Python, occasional C/C++), integrate cameras, stages, lasers, and sensors, implement automated feedback-driven imaging protocols, and contribute to large-scale data acquisition, visualization, and documentation for reproducible experimental systems.

Develop and maintain scalable image-analysis pipelines for large fluorescent microscopy datasets; integrate multimodal data into AI-ready formats; improve segmentation, tracking, stitching, and registration; implement QC metrics and modular processing frameworks; collaborate with biologists and engineers and publish results.

Develop and scale deep learning foundation models and systems for biology. Design model architectures and pre-training pipelines, build post-training discovery systems (RL, reward modeling, reasoning, multi-agent orchestration), create evaluation frameworks tied to biological outcomes, and publish/open-source results while collaborating with scientists worldwide.

Design, build, and maintain scalable data infrastructure and tooling for the full data lifecycle to support AI model training and biomedical research. Partner with researchers to enable performant data pipelines, orchestration, storage, observability, and hybrid cloud/HPC compute workflows.

Manage daily wet-lab operations, inventory, equipment maintenance, and incident response. Develop and enforce safety protocols, SOPs, chemical and biohazard waste handling in coordination with EH&S. Identify operational risks, propose process improvements, support long-term lab planning, and provide general lab management support to scientists and platform leaders.

Lead design and operation of hybrid HPC-AI infrastructure supporting large-scale GPU training, low-latency inference, and data-heavy pipelines. Architect and optimize on-prem and cloud GPU resources, integrate MLOps and deployment pipelines, implement advanced scheduling (Slurm, Kubernetes, SUNK), manage storage and networking, support researchers on performance tuning, and drive adoption of AI tooling across teams.

Operate and scale multi-site multi-GPU clusters running Slurm on Kubernetes: ensure reliability and observability, debug storage/network/GPU failures, automate capacity and lifecycle management, drive configuration-as-code, partner with researchers and vendors, and reduce incident MTTR while enabling multi-thousand GPU training runs.

Perform biochemical and biophysical assays to characterize AI-designed molecules, design and construct plasmids, develop and run protein and cell-based assays, document and analyze results, and collaborate with AI researchers to translate experimental data into research insights.

Design and build petabyte-scale data pipelines and platform tooling to ingest, transform, catalog, and deliver heterogeneous biological datasets for AI research. Improve reliability, observability, and automation (agent-based curation/QA) while collaborating with AI researchers and scientists.

Lead AI safety research for biological applications: design safety evaluations, assess emerging capabilities, partner with scientists to build robust safeguards, inform policy, and communicate results to internal and external stakeholders.

Develop and scale frontier AI systems for biology, including foundation models, RL and multi-agent systems. Design model architectures, pre-training and post-training discovery pipelines, evaluation frameworks, and publish/open-source research with scientific collaborators.