ML Research Scientist - Atomistic Simulation Models

Invent and exploit probabilistic generative models that exploit to Achira’s foundation simulation models for drug discovery to accelerate generative molecular design and biomolecular conformational sampling. 🚀 WHY ACHIRA - Join a world-class team of researchers, scientists, and engineers unifying probabilistic AI/ML and molecular simulation to reimagine small molecule drug discovery. - Advance new architectures for conditional 3D generation and learned proposal mechanisms informed by physical priors. - Operate at the frontier scale of large models, large datasets, and high-throughput evaluation on an ML-framework–native biomolecular simulation stack. - Own impact end-to-end from model conception to sampler design to prospective design tools. - Work in a culture that rewards rigor, speed, and scientific depth with an ownership mindset. ABOUT THE ROLE Achira is building foundation simulation models http://achira.ai and conditional generators for molecular systems. You will design probabilistic generative models (utilizing strategies such as diffusion models, normalizing flows, and flow matching) that that exploit Achira’s next-generation biomolecular simulation potentials. Your work will enable target- and property-conditioned small-molecule generation and efficient exploration of biomolecular conformational landscapes, driving measurable gains in efficiency for small molecule design. Familiarity with statistical mechanics—particularly nonequilibrium statistical mechanics based on Crooks/Jarzynski viewpoints—is desirable, but the center of gravity is probabilistic AI/ML. 🛠️ WHAT YOU’LL DO - Develop conditional molecular generators: Build conditional small-molecule generators (e.g., pocket/scaffold/pharmacophore- and property-conditioned) using generative modeling strategies such as diffusion models, normalizing flows, and flow matching with 3D- and symmetry-aware representations. - Develop efficient samplers: Develop sequential sampling pipelines (e.g. SMC/AIS/tempering/Boltzmann generators) that anneal from learned priors into probabilities induced by Achira’s ML potentials, maximizing ESS and reducing bias/variance. - Couple learning and sampling: Design learned proposal mechanisms (transport maps, score-guided moves) that adapt to stiff, multimodal landscapes and improve mixing and wall-clock efficiency. - Leverage nonequilibrium statistical mechanics: Where beneficial, use nonequilibrium switching protocols and work-based estimators to accelerate exploration and estimate partition-function ratios/affinity proxies. - Measure what matters: Define and track relevant metrics (ESS/compute, acceptance probabilities) and build reliable evaluation harnesses for fast, physics-informed feedback. - Experiment and engineer for reproducibility: Collaborate with our engineering team to implement robust research software in Python (PyTorch and/or JAX), with tests, CI, experiment tracking, and clear documentation. - Collaborate closely: Partner with computational chemistry, AI/ML, and platform teams to shape objectives (potency, selectivity, developability) and run prospective design studies. - Automate workflows: Use generative coding and experiment-management tools to accelerate iteration and close active-learning loops with synthetic data generation in the loop. 🧠 ABOUT YOU - Probabilistic ML background: Deep grasp of probabilistic machine learning, Markov chain Monte Carlo, variational inference, diffusion models, normalizing flows, flow matching, and uncertainty quantification. - Sequential methods expert: Experience with sequential Monte Carlo methods, proposal design, and diagnostics for high-dimensional, multimodal targets. - Geometric intuition: Comfort with graph/point-cloud/SE(3)-aware models and constraints relevant to protein–ligand systems and conformer generation. - Systems thinker: You integrate models into end-to-end pipelines (data → model → sampler → physics-aware evaluation → candidate triage) and care about measurab...