ML Engineer

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