ML Research Scientist - Atomistic Foundation Models

Invent the next generation of deep generative, representational, and simulation models for molecules and materials — building the foundation models that make the atomistic world learnable, predictable, and designable.

• Join a world-class, interdisciplinary team of ML researchers, physicists, chemists, and engineers reimagining atomistic simulation through large-scale foundation models.
• Push the frontier where deep learning meets the laws of nature — bridging generative AI, probabilistic reasoning, and molecular physics.
• Work at a scale few attempt: massive data, massive compute, and massive ambition.
• Own your research end-to-end — from concept and architecture to training, evaluation, and deployment.
• Thrive in a culture that rewards rigor, velocity, creativity, and impact — not bureaucracy.

Achira is building foundation simulation models — large-scale models that learn the structure, dynamics, and energetics of the atomistic world.

These models unify deep representation learning, generative modeling, and advanced simulation and sampling.

As a Generative AI Researcher, you will:

• You develop models that both represent and generate molecular systems, and simulate their dynamics through learned sampling and reasoning.

• Your architectures and algorithms become core components of Achira's foundation model platform.

• You thrive in collaborative interdisciplinary environments.

• You help define the next era of generative and simulation AI for the physical sciences.

At Achira, we're teaching machines to understand and simulate the laws of nature — making matter itself generative, interpretable, and designable.

If you think deeply, build boldly, and dream in equations, tensors, and compute graphs, we want you on our team.

EligibilityIn compliance with United States federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to provide required employment eligibility verification documentation upon hire.

You are a deep learning researcher who moves seamlessly between representation learning, generation, and simulation — motivated by the idea of teaching AI to reason about the physical world.

Required Qualifications

• PhD or equivalent research experience in machine learning, physics, chemistry, computer science, or a related field.

• Proven expertise in deep generative modeling (e.g., diffusion, VAEs, flows, autoregressive transformers).

• Experience in representation learning for structured data, especially graph or 3D geometric models (GNNs, SE(3)/E(3)-equivariant networks, geometric transformers).

• Proficiency in Python and modern ML frameworks (PyTorch, JAX) plus scientific libraries (NumPy, SciPy).

• Solid grounding in probability, optimization, and deep learning fundamentals.

• Demonstrated research impact through publications, open-source contributions, or released models.

• Experience with atomistic simulations, molecular dynamics, or electronic-structure data.

• Familiarity with probabilistic inference, MCMC, variational methods, or reinforcement learning for sampling and control.

• Experience integrating physics-informed priors or energy-based models into deep architectures.

• Knowledge of atomistic molecular datasets and benchmarks such as SPICE, OMol25, QCML, AIMNet2

• Experience scaling models on HPC or distributed GPU infrastructure.

• Strong technical communication across interdisciplinary teams.