ML Research Scientist (MLRS) - Generative AI
Confirmed on the employer's own hiring board on Nov 5, 2026. First seen by Alion on Jun 30, 2026. Achira scores B on the Alion truth index.
Why Achira
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.
About the Role
We’re looking for machine learning researchers who want to shape the frontier of generative models for the atomistic microcosm. You will work at the intersection of cutting-edge machine learning, statistical mechanics, and approximate bayesian inference to help us conquer sampling and generation problems at light-speed. In addition, you’ll collaborate with experts in chemistry and physics to invent and implement models and applications to unlock what’s possible for Achira’s microscopic 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.
What You’ll Do
Invent advanced sampling and simulation methods that integrate probabilistic inference, deep learning, and reinforcement learning to enable efficient exploration and simulation of learned energy landscapes for molecular systems.
Design and train frontier generative models: diffusion, autoregressive, flow-based, and latent-variable architectures.
Build models that can map between data distributions to bridge the gap between simulation and reality.
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.
About You
Interested in building generative models that describe real matter.
Drive to build at the frontier of what’s possible and try out new, high-risk ideas.
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.
Nice to Have
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!
Machine learning researcher with professional experience (post-degree) in an industry setting.
Experience working with models that operate on 3-D point clouds and dynamic data.
Experience in sequential monte carlo methods.
Experience with probabilistic programming.
Experience with pre-training, mid-training, and post-training (especially reinforcement learning) parts of the model development process.
Familiarity with statistical mechanics: working knowledge of sampling, estimators, and the Crooks/Jarzynski perspective of nonequilibrium statistical mechanics.
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.