ML Research Scientist – Atomistic Simulation Models

Role Overview

• 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.

Requirements

• PhD (or equivalent research experience) in computer science, statistics, applied math, computational chemistry/biology, or related field.
• Demonstrated track record in probabilistic ML and generative modeling (publications, impactful open-source, or deployed systems).
• Industry experience tackling practical machine learning problems.
• Hands-on experience with diffusion/flows/flow matching on structured or geometric data.
• Practical experience with sequential Monte Carlo/AIS/tempering and/or advanced MCMC.
• Proficiency in Python with PyTorch and/or JAX; strong software engineering hygiene.
• Familiarity with biomolecular structure and data representations (graphs/3D/SMILES).

Tech Stack

• Python
• PyTorch
• Switching

Benefits

• Offers Equity
• Offers Bonus