About this role
Senior Scientist focused on AI Agent Systems for drug development, integrating multimodal data via LLMs and agent architectures to accelerate discovery and development.
Key Responsibilities
- Develop multimodal reasoning systems; build agent memory architectures; evaluate uncertainty quantification approaches; publish results; mentor team
Technical Overview
Research leadership in AI/ML with emphasis on multimodal reasoning, agent memory, uncertainty quantification, and deployment-ready frameworks (LangGraph, DSPy, mem0); uses PyTorch/TensorFlow and Python.
Ideal Candidate
A senior AI/ML scientist with a PhD and 2+ years of domain experience in drug discovery, skilled in advanced AI techniques, agent-based reasoning, and open-source frameworks; able to lead cross-disciplinary teams in a hybrid European setting.
Must-Have Skills
A Ph.D. degree in AI/ML or related field (e.g.computer sciencemachine learningdata scienceapplied mathematicsstatistics) with at least two (2) years working experienceKnowledge in Bayesian inferenceuncertainty quantificationinformation theorycausal inferencinggraph neural networksProficiency with open-source agentic frameworks (e.g.LangGraphDSPymem0etc.) and deep learning frameworks (e.g.PyTorchTensorflowetc.)Programming in Python
Nice-to-Have Skills
Understanding of drug discovery pipelineExperience in chemical/biological fieldsCloud hosting (AWS and/or Azure)
Required Skills
PhD in AI/ML; Bayesian inference; uncertainty quantification; information theory; causal inferencing; graph neural networks; LangGraph; DSPy; mem0; PyTorch; TensorFlow; Python; LLMs
Hard Skills
Ph.D. in AI/ML or related fieldBayesian inferenceUncertainty quantificationInformation theoryCausal inferencingGraph neural networksLangGraphDSPymem0PyTorchTensorFlowPythonLLMs
Soft Skills
Domain expertise in AI for drug discoveryIndependent and collaborative workClear communicationMentoring
Keywords for Your Resume
senior scientistai agent systemsdrug developmentmultimodal reasoningagent memory architecturesuncertainty quantificationbayesian inferenceinformation theorycausal inferencinggraph neural networksLangGraphDSPymem0PyTorchTensorFlowPythonLLMscloud hostingawsazureai/mldata scienceBayesian inferenceUncertainty quantificationGraph neural networksDrug discovery
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