Additional Preferences: • Proficiency in Python and deep experience with ML/Deep Learning frameworks (e.g., PyTorch, Tensorflow, JAX, HuggingFace) • Hands-on experience building agentic AI systems (e.g., LangChain, OpenAI Agents SDK) • Experience designing and shipping end-to-end systems in cloud environments (backend APIs, lightweight frontends, and agentic platforms) • GitHub portfolio a plus • Strong DevOps/engineering skills: version control (git), containerization (docker, kubernetes), GitOps + CI/CD practices, data systems (Redis, SQL/NoSQL), unit testing, frontend (streamlit, flask) • Working knowledge of cloud-native (AWS/Azure) pipeline architectures including Nextflow, Argo on Kubernetes • Familiarity with MLOps, including model versioning, data versioning, and continuous integration/continuous deployment for ML systems • Experience with LLM post-training, fine-tuning, or RLHFDemonstrable research experience, evidenced by contributions to projects, and ideally through publications in relevant ML/NLP venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP) • Experience mentoring and guiding junior researchers or engineers. Responsibilities: • Research & Innovation • Partner with chemists and biologists to translate scientific workflows into agentic systems • Deploy and integrate Agentic AI system into active research programs • Design and implement cloud-native data pipelines connecting lab instruments, databases, and AI models • Support model deployment, inference services, and experiment tracking (e.g., MLflow) • Integrate LLM reasoning with domain tools (RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) to build composite agents that plan, simulate, and execute DMTA tasks • Prototype and iterate rapidly on agent planning strategies, memory systems, and human-in-the-loop patterns.