Senior Applied AI Researcher, Digital Biology

We are seeking innovative and driven Applied Researchers in the field of deep learning. We are looking for candidates with a consistent track record of research excellence; a broad perspective across the fields of artificial intelligence; and deep experience in machine learning, applied science, and computational medicine. We are a world leader in high-performance and mobile computing technology for AI, with ambitious plans for future systems. This position offers the opportunity to have a real impact in an applied research-focused team in a dynamic company.
The Applied AI Architecture group focuses on applied research. As a member of the team, you will publish your work and share code as an open source. In addition, you will join a groundbreaking initiative at the intersection of AI and biology. This collaborative project aims to develop both foundational and generative AI models, as well as agentic AI systems, redefining our understanding of complex biological systems and enabling advanced diagnostics and novel scientific insights, while strengthening our Health Platform ecosystem.
What You'll Be Doing:
Conceptualize, design, and implement novel deep learning architectures for biological data, with a strong focus on large-scale models such as Large Language Models (LLMs), Transformers, and State Space Models (SSMs).
Develop multimodal learning systems that integrate heterogeneous data types (e.g., clinical time-series, imaging, genomics, and text) for improved representation and prediction.
Develop both foundational and generative models and agentic AI systems, including multi-step reasoning, tool use, and autonomous decision-making capabilities.
Develop digital twin systems for healthcare, combining mechanistic models, physiological data, and AI to simulate disease progression, treatment response, and patient-specific trajectories.
Implement deep learning systems integrated with agents, enabling end-to-end workflows that combine learning, planning, and execution.
Evaluate model performance, analyze results, and iterate on designs to achieve optimal outcomes.
Apply your knowledge of distributed training to build high-quality code for training, optimizing, and deploying large-scale models, while managing complex datasets.
Collaborate closely with a diverse team of researchers, bioinformaticians, and domain experts in a highly interdisciplinary environment.