Advisor - Agent Research

Role Summary

We are rebuilding the Design-Make-Test-Analyze (DMTA) cycle, infusing scientific automation with foundation models, multi-agent systems, and robotics to make scientific discovery intelligent, autonomous, and fast. We're seeking a scientist-engineer hybrid to deploy AI-driven discovery platforms directly with portfolio research teams, bridging cutting-edge agentic AI systems and real-world drug discovery workflows.

Responsibilities

• 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

Qualifications

• Required: PhD (or MS + 2 yrs / BS + 4 yrs equivalent experience) in Bioinformatics, Cheminformatics, Computer Science, or related discipline with demonstrated wet-lab collaboration or experience
• Required: Approximately 1–2 years of demonstrated experience applying AI/ML in a scientific discipline such as biology, chemistry, neuroscience, or a related field (industry postdoc counts)
• Preferred: Proficiency in Python and deep experience with ML/Deep Learning frameworks (e.g., PyTorch, TensorFlow, JAX, HuggingFace)
• Preferred: Hands-on experience building agentic AI systems (e.g., LangChain, OpenAI Agents SDK)
• Preferred: Experience designing and shipping end-to-end systems in cloud environments (backend APIs, lightweight frontends, and agentic platforms) - GitHub portfolio a plus
• Preferred: Strong DevOps/engineering skills: version control (git), containerization (Docker, Kubernetes), GitOps + CI/CD practices, data systems (Redis, SQL/NoSQL), unit testing, frontend (Streamlit, Flask)
• Preferred: Working knowledge of cloud-native (AWS/Azure) pipeline architectures including Nextflow, Argo on Kubernetes
• Preferred: Familiarity with MLOps, including model versioning, data versioning, and CI/CD for ML systems
• Preferred: Experience with LLM post-training, fine-tuning, or RLHF
• Preferred: Demonstrable research experience, evidenced by contributions to projects and publications in ML/NLP venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP)
• Preferred: Experience mentoring and guiding junior researchers or engineers

Skills

• Python programming and ML/Deep Learning frameworks (PyTorch, TensorFlow, JAX, HuggingFace)
• Building agentic AI systems (e.g., LangChain, OpenAI Agents SDK)
• Designing and shipping end-to-end cloud-based systems (backend APIs, lightweight frontends, agentic platforms)
• DevOps and software engineering practices (git, Docker, Kubernetes, GitOps, CI/CD, data systems)
• Cloud-native pipeline architectures (AWS/Azure) and workflow tools (Nextflow, Argo on Kubernetes)
• MLOps fundamentals (model/data versioning, CI/CD for ML)
• LLM post-training, fine-tuning, or RLHF
• Research track record or publications in ML/NLP venues
• Mentoring and guiding junior researchers or engineers

Education

• PhD in Bioinformatics, Cheminformatics, Computer Science, or related discipline (or MS + 2 yrs / BS + 4 yrs equivalent experience)