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

Achira • San Francisco, CA, US

Actively Hiring

Full-time $164k - $259k Posted 24 days ago

Role overview

check_circle Build a robust pre-, mid-, and post-training curriculum that ensures foundation model performance and impact.
check_circle Create reenforcement learning strategies to help models focus their capacity where it matters most, especially when the training data doesn’t cover the domain of applicability.
check_circle 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.
check_circle Prototype, benchmark, and iterate rapidly to transform research ideas into reusable and scalable components across Achira’s ecosystem.
check_circle Collaborate with physicists and chemists to ensure models are grounded in real physics.
check_circle Work with research engineers and the infrastructure team to identify where research ideas will need support in order to deliver effective results.

Experience with self-supervised representation learning techniques.
Experience with Bayesian deep learning techniques and uncertainty quantification.
Experience developing or applying generative models for 3D systems.
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.

check_circle Drive to apply modern ML techniques to solve problems at the frontier of the microscopic world.
check_circle A willingness to follow the data and embrace an empirical approach to the bitter lesson.
check_circle A pragmatic approach to inductive bias (eg. physical priors, equivariance) in model building.
check_circle Experience designing, running and analyzing ML experiments at scale.
check_circle Experience with 3D geometric deep learning.
check_circle Machine learning researcher with professional experience (post-degree) in an industry setting.
check_circle Demonstrated research impact through conference talks or publications (in machine learning venues), open-source contributions, or released models.
check_circle Strong interdisciplinary communication and presentation skills and the ability to translate ideas and concepts to colleagues from non-ML backgrounds.
check_circle Proficiency in Python and modern ML frameworks (PyTorch, JAX).
check_circle Experience collaborating on research projects across multi-person teams.

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