Jobs at Biohub

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.

Leads scaling of a live-imaging and data-generation pipeline for developing organisms. Responsibilities include setting throughput and quality targets, identifying bottlenecks, improving sample preparation and light-sheet microscopy, coordinating image-processing and compute workflows, establishing SOPs and QC gates, mentoring team members, and documenting open scientific methods. The role requires hands-on work across microscopy, biology, computation, and quantitative image analysis.

Lead design and implementation of company-wide security architecture, perform penetration testing and vulnerability management, automate security tooling and CI/CD scanning, build observability and incident response systems, and collaborate across teams to scale security for AI compute and research infrastructure.

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 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.

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.

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.

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.

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.

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.

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 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.

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.

Lead AI safety research for biological models: design and run safety evaluations, build robust safeguards against misuse, monitor capability advances, partner with internal and external experts, and shape safety policy and communications.

Lead and coordinate multidisciplinary imaging science and technology programs, develop and execute project plans, manage technical dependencies, track metrics and progress, communicate with stakeholders, mitigate risks, and document outcomes and best practices to deliver complex AIxBio and imaging initiatives aligned with the strategic roadmap.

Design and evaluate imaging data representations and tokenization strategies for biological AI models. Coordinate across experimental, data science, engineering, and research teams to prioritize data acquisition, define quality criteria, and combine heterogeneous modalities into robust training frameworks that improve model performance and scientific interpretability.