ML Engineer Monarch - 3.4 Emeryville, CA Job Details Full-time 11 hours ago Qualifications Software engineering Production systems Full Job Description We offer opportunities to do your life's work while helping solve one of the most important technical and moral challenges of our time. Full-time, in-office in Emeryville, California. Compensation includes equity. Build the reliable systems that carry our data from an assay recording to a reproducible model, an evaluated prediction, and a usable recommendation for the next experiment. Key Responsibilities Own pipelines for ingesting, validating, versioning, and joining assay videos, metadata, compound records, model features, and experimental outcomes Build reproducible training and evaluation infrastructure with clear data lineage, model versioning, automated tests, and auditable outputs Turn research prototypes into dependable batch and online systems that can rank compounds and surface recommendations through our tools Monitor data quality, distribution shift, calibration, latency, cost, and failures as the number of labs and assays grows Design interfaces between computer vision, molecular models, active-learning systems, and the lab workflow Improve developer and researcher velocity without weakening scientific reproducibility or access controls Qualifications Strong production software engineering experience in Python and modern machine-learning or data systems Experience deploying and operating model-training, feature, evaluation, or inference pipelines in a cloud environment Fluency with testing, observability, data validation, version control, and reproducible computational workflows Ability to work with large video datasets and structured scientific data Ability to collaborate closely with researchers while making sound engineering tradeoffs Desired Attributes Experience with PyTorch, JAX, or TensorFlow and workflow-orchestration tools Experience on Google Cloud or with large-scale object-storage pipelines Familiarity with computer vision, molecular machine learning, active learning, or scientific data platforms Instinct for simple systems, explicit failure modes, and measurable reliability