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

At Achira, we are building a team of world-class scientists, ML researchers, and engineers to move beyond the beaten path and build a frontier lab for Physical AI for molecules. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed.

At Achira, you’ll operate at the frontier scale of massive compute, massive data, and massive ambition. You’ll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We’re looking for new members who share our sense of relentless urgency and are natural collaborators who value team success.

We’re looking for deep learning researchers who want to build rich representations of atomistic systems to help teach AI about the microscopic world. You will collaborate with domain experts in molecular modeling to develop Achira’s next-generation foundation models. 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. Travel is part of all roles at Achira, both to conferences and corporate on-site activities.

• Build a robust pre-, mid-, and post-training curriculum that ensures foundation model performance and impact.

• 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.

• 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.

• Prototype, benchmark, and iterate rapidly to transform research ideas into reusable and scalable components across Achira’s ecosystem.

• 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 willingness to follow the data and embrace an empirical approach to the bitter lesson.

• A pragmatic approach to inductive bias (eg. physical priors, equivariance) in model building.

• Experience designing, running and analyzing ML experiments at scale.

• 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.

• 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.

• Desire and comfort with working on frontier problems in physical AI to invent the blueprint for how they will be tackled.

Achira values excellent ML researchers from many backgrounds, and expect members of the team to contribute complementary strengths. If the work excites you, we encourage you to apply, even if you hit none of the bonus features listed below!

• 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.