Advisor - Agent Research

Partner with scientists to build autonomous agents for molecule discovery tasks Design and build reinforcement learning environments for real discovery tasks with appropriate state, action, and termination semantics Curate and engineer reward functions from noisy scientific signals Post-train domain models using SFT, DPO, GRPO, PPO, reward modeling, and distillation on chemistry and biology tasks Integrate learned policies with domain tools, including RDKit, molecular graph ML, ELN/LIMS APIs, and instrument drivers Build evaluation infrastructure, including task suites, scoring harnesses, regression tracking, and experiment tracking such as MLflow Represent Frontier AI in the broader View email address on click.appcast.io and external AI research community Publish research, give talks, review papers, and scout emerging trends Evaluate external vendors, open-source projects, and academic collaborations for strategic fit Develop models and infrastructure that improve autonomous scientific discovery and reduce DMTA turnaround Transition AI prototypes into production-deployed systems Requirements PhD in Machine Learning, Bioinformatics, Cheminformatics, Computer Science, or related discipline, or MS plus 3 years / BS plus 5 years equivalent experience Demonstrated wet-lab collaboration or hands-on experience Approximately 1–2 years of experience applying AI/ML in biology, chemistry, neuroscience, or a related scientific field; industry postdoc counts Hands-on experience training or post-training AI models Proficiency in Python Deep experience with ML/deep learning frameworks such as PyTorch, TensorFlow, JAX, and HuggingFace Experience with reinforcement learning and post-training methods, including PPO, GRPO, DPO, reward modeling, RLHF/RLAIF, and libraries such as TRL or verl Familiarity with molecular representation learning, generative chemistry, or protein/nucleic acid models Hands-on experience building agentic AI systems Experience designing and shipping end-to-end systems in cloud environments Working knowledge of cloud-native AWS/Azure pipeline architectures, including Nextflow and Argo on Kubernetes Demonstrable research experience through project contributions and ideally publications in relevant ML/NLP venues Experience mentoring and guiding junior researchers or engineers Core Competencies Expertise in building and integrating autonomous agents for scientific discovery using reinforcement learning and machine learning frameworks. Proficient in Python and cloud-native architectures, with a strong background in collaborating with scientists and mentoring junior researchers. Highest-signal resume keywords PhD In Machine Learning Proficiency In Python Experience With Reinforcement Learning Deep Experience With ML Frameworks Hands-On Experience Training AI Models ATS Optimization Keywords Hard Skills Reinforcement Learning Machine Learning Post-Training Methods Molecular Representation Learning Generative Chemistry Agentic AI Systems End-To-End System Design Cloud Environments AI Model Training Reward Modeling Soft Skills Collaboration Mentoring Research Communication Industry Keywords Bioinformatics Cheminformatics Molecule Discovery Scientific Discovery Neuroscience Chemistry AI Research ML/NLP Venues Tools & Technologies PyTorch TensorFlow JAX HuggingFace MLflow AWS Azure Nextflow Argo Kubernetes #J-18808-Ljbffr Jobtailor