Associate Director / Director, Soft Matter Formulation

Your Impact at Lila Lead Lila’s strategy and execution at the intersection of soft matter, molecular chemistry, and AI. You will build closed-loop, active-learning systems that design, optimize, and scale formulations—polymers, colloids, emulsions, gels, electrolytes, inks, coatings, and adhesives—by unifying data, physics, and automation. Your work will shorten iteration cycles, deliver robust recipes with target properties, and enable real-world deployment across multiple product domains. What You'll Be Building Formulation Intelligence Platform Create multi-objective, constraint-aware active learning and Bayesian optimization pipelines that map recipe + process → properties and enable inverse design with uncertainty quantification. Data and Representations Stand up clean, linked datasets and ontologies for components, concentrations, processing, and measurements; develop polymer/mixture representations (e.g., BigSMILES, graph/3D features) and feature stores for reuse. Physics- and Simulation-Informed Modeling Fuse ML with soft-matter physics: solubility and mixing rules, Flory–Huggins, CMC/cloud points, cure kinetics, rheology models; couple mesoscale simulation (DPD/CGMD, SCFT, phase-field) and fast surrogates for phase behavior, stability, rheology, transport, and interfacial properties. Autonomous Experimentation Partner with lab automation to run high-throughput DoE and adaptive sampling; integrate LIMS/ELN, instrument control, and standardized protocols for rapid validate–learn loops. Real-World Use Cases and Deployment Deliver validated formulations and SOPs for priority applications (e.g., coatings/adhesives, printable inks/pastes, polymer/gel electrolytes, emulsions/hydrogels); harden models for scale-up, robustness, and manufacturability. Leadership and Collaboration Lead a small cross-functional team (ML scientists, formulation chemists, automation engineers); define roadmaps, KPIs, and best practices; partner closely with product, operations, and manufacturing. What You’ll Need to Succeed Advanced degree in Chemical Engineering, Materials/Polymer Science, or Chemistry (PhD preferred) and 7+ years RD experience, including 3+ years applying ML/active learning to formulations or soft matter. Deep understanding of soft-matter fundamentals: phase behavior, self-assembly, rheology, interfacial phenomena, diffusion/transport, and cure/crosslinking. Hands-on formulation and characterization experience (polymers, colloids, surfactants, fillers/additives); familiarity with rheometry, DSC/TGA/DMA, DLS/zeta, scattering/spectroscopy. Proficiency in Python and ML: Bayesian optimization, DoE, Gaussian processes, multi-fidelity modeling, constraint handling, UQ; tools like PyTorch/JAX, scikit-learn, BoTorch/Ax; solid data engineering practices and experiment tracking. Facility with mesoscale/continuum simulation and surrogates: DPD/CGMD (LAMMPS/HOOMD/OpenMM), SCFT/phase-field, mixing and transport models, and their integration with ML. Excellent communication, stakeholder management, and experience leading cross-functional RD to delivery. Bonus Points For High-throughput/robotic formulation experience and automated characterization workflows. Sector expertise in energy storage electrolytes/ionogels, printable electronics, coatings/adhesives/sealants, consumer/personal care, or biomaterials. Experience deploying models to production (MLOps, cloud/HPC orchestration) and maintaining performance in the wild. Publications, patents, or open-source contributions in soft matter, formulation science, or scientific ML. Knowledge of EHS and regulatory considerations relevant to formulations (e.g., REACH, FDA-adjacent contexts). About Lila Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We ar ... (truncated, view full listing at source)