Design and train frontier deep generative models — diffusion, autoregressive, flow-based, and latent-variable architectures — for molecules, materials, and atomic systems.
Develop expressive representations of molecular and atomistic structure and dynamics, including equivariant graph neural networks, geometric transformers, and latent encoders that capture physical symmetries and constraints.
Invent advanced sampling and simulation methods that integrate probabilistic inference, deep learning, and reinforcement learning — enabling efficient exploration and simulation of learned energy landscapes.
Build models that understand, generate, and simulate the physical world — unifying reasoning, simulation, and prediction.
Collaborate with physicists and chemists to ground models in ab initio, molecular dynamics, and experimental data.
Prototype, benchmark, and iterate rapidly — transforming research ideas into reusable, scalable model components across Achira’s foundation model stack.
Contribute to publications, open-source tools, and internal research projects that advance the field.

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.

ML Research Scientist – Atomistic Foundation Models

Key skills