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Senior Applied AI Researcher, Digital Biology
В офисеПолная занятостьAI & Machine LearningElectronics Manufacturing
Join an applied research team developing scalable AI systems for biological and healthcare applications. The role combines deep learning, multimodal modeling, generative and agentic AI, and digital twins.
Обязанности
- Design and implement novel deep learning architectures for biological data, including large-scale language and generative models.
- Develop multimodal systems integrating clinical time series, imaging, genomics, and text.
- Build foundational, generative, and agentic AI systems with reasoning, tool use, planning, and autonomous decision-making.
- Develop healthcare digital twins that combine mechanistic models, physiological data, and AI to simulate disease progression and treatment response.
- Integrate deep learning systems with agents to support end-to-end learning, planning, and execution workflows.
- Evaluate model performance, analyze results, and iterate on system designs.
- Write high-quality code for training, optimizing, and deploying large-scale models while managing complex datasets.
- Collaborate with researchers, bioinformaticians, engineers, and clinical or other domain experts.
- Publish research and share code through open-source projects.
Требования
- PhD in machine learning, computer science, engineering, or a related discipline.
- At least 8 years of hands-on experience developing, training, and deploying deep learning models at scale.
- Strong expertise in deep learning, distributed training, optimization, and inference.
- Experience with large language models, Transformers, state space models, or generative models.
- Strong programming skills in Python and C++, with experience using PyTorch or CUDA.
- Track record of independent research, robust implementation, rigorous evaluation, and publications or presentations at leading conferences.
- Strong communication and collaboration skills in a research-driven, interdisciplinary environment.
Будет плюсом
- Experience with agentic AI systems, including retrieval-augmented generation, tool use, planning, or multi-agent architectures.
- Experience with multimodal models combining vision, language, structured, or time-series data.
- Background in bioinformatics, digital biology, clinical research, or interdisciplinary healthcare projects.
- Experience with digital twins, simulation frameworks, or data-driven healthcare modeling.
- Experience with large-scale ML data pipelines and distributed frameworks.
- Mentoring experience.