Achira

ML Research Scientist (MLRS) - Representation Learning for Molecular AI

San Francisco Office

Sponsorship not specifiedDetected 22 days ago

PythonMachine LearningDeep LearningPyTorchNLPResearchCommunicationCollaboration

About the role

You'll work at the intersection of model architecture, data, and training strategy to find new points on the Pareto frontier of representational richness, robustness, accuracy, and speed for microscale world models.
While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed.
Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers.

Build a robust pre-, mid-, and post-training curriculum that ensures foundation model performance and impact.
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.
Collaborate with physicists and chemists to ensure models are grounded in real physics.
Work with research engineers and the infrastructure team to identify where research ideas will need support in order to deliver effective results.
Drive to apply modern ML techniques to solve problems at the frontier of the microscopic world.
A pragmatic approach to inductive bias (eg. physical priors, equivariance) in model building.

Experience designing, running and analyzing ML experiments at scale.
Strong interdisciplinary communication and presentation skills and the ability to translate ideas and concepts to colleagues from non-ML backgrounds.
Proficiency in Python and modern ML frameworks (PyTorch, JAX).
Experience collaborating on research projects across multi-person teams.

Achira values excellent ML researchers from many backgrounds, and expect members of the team to contribute complementary strengths.
Experience with equivariant graph neural network architectures (NequIP, MACE, SchNet, PaiNN, or similar).
Prior experience working in or with researchers in the domains of computational chemistry, biology, or materials science.
Experience working with multi-cloud distributed compute systems.
Experience working with multi-site distributed company team.

Create reinforcement learning strategies to help models focus their capacity where it matters most, especially when the training data doesn't cover the domain of applicability.
Experience with 3D geometric deep learning.
Machine learning researcher with professional experience (post-degree) in an industry setting.
Demonstrated research impact through conference talks or publications (in machine learning venues), open-source contributions, or released models.

This listing is sourced directly from Achira's careers page and normalized into a canonical job model.