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Achira · 2 months ago

Generative AI Researcher - Atomistic Simulation Models

San Francisco Office

Full-time

Onsite

Senior Level

$165K/yr - $259K/yr

Achira is a company focused on unifying probabilistic AI/ML and molecular simulation for drug discovery. The role involves designing and developing probabilistic generative models and efficient sampling pipelines to enhance small-molecule generation and exploration of biomolecular landscapes.

Artificial Intelligence (AI)BiotechnologyMedical

Responsibilities

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

Qualification

Probabilistic machine learningGenerative modelingSequential Monte CarloPython with PyTorchDiffusion modelsNormalizing flowsFlow matchingStatistical mechanicsGeometric deep learningActive learningBayesian optimizationEngineering disciplineCollaboration

Required

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)

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)

Preferred

Experience with ML interatomic/energy potentials is a bonus

Background in SE(3)-equivariant architectures, geometric deep learning, or score matching on manifolds

Experience with active learning / Bayesian optimization or RL-style acquisition for proposal selection

Experience with implementing MCMC sampling approaches grounded in statistical mechanics—especially nonequilibrium approaches that utilize Crooks/Jarzynski—a plus

Contributions to open-source scientific software; experience mentoring or leading small research efforts