onsite
ML Research Scientist - Atomistic Simulation Models - achira
Research Engineer
Lead the design and deployment of probabilistic generative models for 3D molecular structures, accelerating drug discovery through advanced conditional generation and physics-informed proposal mechanisms on a high‑throughput ML‑native simulation stack.
About the role
Key Responsibilities
- Invent and refine probabilistic generative models that integrate with foundation simulation frameworks for drug discovery.
- Develop conditional 3D generation architectures and learned proposal mechanisms guided by physical priors.
- Operate at the frontier of large‑scale models, datasets, and high‑throughput evaluation on an ML‑framework–native biomolecular simulation stack.
- Own end‑to‑end impact from model conception to deployment, ensuring reproducibility and scalability.
- Collaborate with cross‑functional teams of researchers, scientists, and engineers to translate research into production‑ready solutions.
Requirements
- PhD or equivalent experience in machine learning, computational chemistry, or related fields.
- Strong background in probabilistic modeling, generative models, and 3D molecular representation.
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Experience with large‑scale data pipelines and high‑throughput evaluation.
- Excellent communication skills and a track record of publishing impactful research.