About the role
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
To learn more, visit monarchlabs.org