ML Research Scientist (MLRS) - Generative AI

Achira; San Francisco (CA); Sur place; USD 110 000 - 150 000; Plein temps

Résumé du poste

Achira is seeking machine learning researchers to innovate in generative models for atomic microcosm. You'll work on cutting-edge machine learning methods in collaboration with physicists and chemists to develop models for molecular systems.

This hybrid role is based in San Francisco or New York City, requiring potential travel. Ideal candidates will have a strong background in Python and ML frameworks.

Qualifications

• Professional experience in machine learning research in an industry setting.
• Ability to collaborate on multi-person research projects.
• Experience in building generative models.

Responsabilités

• Invent advanced sampling and simulation methods for molecular systems.
• Design and train generative models including diffusion and autoregressive architectures.
• Collaborate with physicists and chemists to ensure models are grounded in real physics.

Connaissances

Proficiency in Python

Experience with PyTorch

Strong interdisciplinary communication skills

Research impact through publications

Description du poste

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.
• 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.

Nice to Have

• 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.