🛠 Tech Stack

💼 About This Role

You'll advance foundation models for molecular simulation at a well-funded AI x chemistry startup. You'll own impactful work end-to-end, from ideation to deployment on distributed infrastructure, working with a world-class team to push the boundaries of AI for drug discovery.

🎯 What You'll Do

• Design and run experiments to test hypotheses for foundation model development.
• Engineer meaningful evals and metrics for rapid model iteration.
• Build scalable, reproducible libraries for training and evaluation.
• Implement model architectures from literature and in-house research.

📋 Requirements

• Strong software engineering fundamentals with reproducible pipelines.
• Track record of observable artifacts (GitHub, papers) in ML or scientific computing.
• Solid working knowledge of PyTorch and JAX.
• Comfortable with HPC or large-scale compute environments.

✨ Nice to Have

• Experience with equivariant architectures or geometric deep learning.
• Familiarity with generative modeling (diffusion, flow matching).
• Regular involvement in open-source ML or scientific computing libraries.

🎁 Benefits & Perks

• 💰 Competitive compensation
• 🏥 Health insurance
• 📈 Equity
• 🏢 Hybrid work (SF or NYC)

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3On-site Interview· 4 hours