ML Research Scientist (MLRS) - Generative AI

Achira • New York, NY, US

Actively Hiring

Full-time $164k - $259k Posted 2 months ago

Role overview

check_circle 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.
check_circle Design and train frontier generative models: diffusion, autoregressive, flow-based, and latent-variable architectures.
check_circle Build models that can map between data distributions to bridge the gap between simulation and reality.
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 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 reenforcement 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.

check_circle Interested in building generative models that describe real matter.
check_circle Drive to build at the frontier of what’s possible and try out new, high-risk ideas.
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.

Fulltime Ai Machine Learning Generative Ai