Machine Learning Researcher

Next Phase Recruitment; California; On-site; USD 120,000 - 210,000; Full time

Job summary

Next Phase Recruitment is seeking an ML Research Scientist to join a frontier-stage organization focused on Physical AI for molecules, combining deep learning with molecular and atomistic modeling.

You will work with a world-class team of ML researchers, engineers, and scientists, tackling representation learning and foundation model development at massive scale.

Qualifications

• PhD or equivalent in CS, ML, physics, chemistry, or a related field.
• Proven experience running ML research at scale from data handling to training and validation.
• Strong background in 3D geometric deep learning or representation learning.
• Proficiency in Python and PyTorch or JAX.

Responsibilities

• Design advanced representation models for molecular/atomistic systems.
• Build pre, mid, and post-training pipelines to optimize foundation models.
• Apply reinforcement or self-supervised learning to improve data efficiency.
• Collaborate with physicists and chemists to align ML with physical principles.
• Prototype, benchmark, and scale new architectures across the research stack.

Skills

3D geometric deep learning

representation learning

ML research at scale

cross-disciplinary collaboration

Education

PhD or equivalent in CS/ML/Physics/Chemistry

Tools

Python

PyTorch

JAX

Job description

My client is a well-funded, frontier-stage organization developing Physical AI for molecules, integrating deep learning with molecular and atomistic modeling.

This is a chance to join a world-class team of ML researchers, engineers, and scientists working at massive scale in compute, data, and ambition. The environment suits those who thrive in fast moving, high-impact research settings.

The Opportunity

We're seeking an ML Research Scientist passionate about representation learning and molecular modeling to help develop next generation foundation models for molecular AI.

You'll work at the intersection of model architecture, training strategy, and physical reasoning, creating systems that push the limits of molecular understanding.

Key responsibilities include:

• Designing and implementing advanced representation models (e.g., GNNs, geometric transformers) for molecular and atomistic systems.
• Developing pre, mid, and post training pipelines to optimize foundation model performance.
• Applying reinforcement or self supervised learning strategies to maximize data efficiency.
• Collaborating with physicists and chemists to ensure ML models align with physical principles.
• Prototyping, benchmarking, and scaling new architectures across the internal research ecosystem.

About You

You bring both technical excellence and scientific curiosity, with experience bridging ML theory and physical application domains.

Requirements:

• PhD or equivalent experience in Computer Science, Machine Learning, Physics, Chemistry, or related field.
• Proven experience running ML research at scale, from data handling to training and validation.
• Strong understanding of 3D geometric deep learning or representation learning methods.
• Proficiency in Python and frameworks such as PyTorch or JAX.
• Ability to communicate and collaborate across interdisciplinary teams.

Nice to Have

• Experience with self supervised or generative modeling approaches.
• Familiarity with equivariant GNNs (NequIP, MACE, SchNet, PaiNN, etc.).
• Background in computational chemistry, materials science, or biophysics.
• Experience working on distributed compute or multi site ML research projects.

Interested?

You're a Machine Learning Researcher ready to work on frontier problems in molecular AI, I'd love to speak with you.