We’re fundamentally building a system that replaces a lab technician + physics with a single neural network. This role exists to help build the backbone of that: building foundational biochemistry models and the continuous data feedback loop between model predictions and physical experiments. This role doesn’t require biology knowledge, just solid fundamentals in machine learning. What you’ll work on: Use your understanding of machine learning to develop models foundational to eventually replacing the need for wet-lab experiments. Design, train, and refine biomolecular interaction models. Assemble high-quality training data and build autonomous data collection systems. Analyze the generalization and weaknesses in our models to inform data-generation strategy. Lead the design of future models to replace every part of chemical R&D. Requirements: Solid practical ML engineering and software engineering fundamentals (Python, PyTorch/JAX/TensorFlow, NumPy/SciPy) Proven track record of designing, building, and validating ML models / products. Strong foundation in mathematics, algorithms, statistics, and data science principles. Familiar with SQL and building datasets for ML. Nice to have: Experience working with biochemical or structural biology datasets Exposure to foundation models in bio (e.g., AlphaFold, ESM, RoseTTAFold, ProtBert). Experience working with LLM agents Experience managing large training runs on GPU clusters. What we offer: Work directly with both founders in a small highly technical fast paced ML engineering team Ownership of foundational ML infrastructure and research A mission that matters: enabling a safer, more sustainable future in agriculture through better models.

#J-18808-Ljbffr