Top Tech Jobs & Startup Jobs

Designs, prototypes, tests, and troubleshoots high-throughput single-cell screening and point-of-care medical devices. The role combines device engineering, cell biology, immunology, materials science, and reagent formulation. Responsibilities include developing controlled biochemical processes, handling biological specimens, validating assays, establishing test protocols, documenting work, and contributing to publications and patents. The engineer independently drives projects from proof of concept through prototype development in a collaborative laboratory environment.

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.

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 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.

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.

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.