ML Engineer

Monarch•Emeryville, CA

11d•$160,000 - $260,000•Onsite

About The Position

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. $160,000–$260,000 annual base salary plus 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.

Requirements

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

Nice To Haves

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

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