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# ML Engineer

- **Company:** Monarch
- **Location:** Emeryville, CA, United States
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Automation of Tests, Cloud Computing, Data Validation, Data Systems, Python (Programming Language), Machine Learning, Tensorflow, Management of Software Versions, Google Cloud, Pytorch, Delivery Pipeline, Data Lineage, Data Management, Software Version Control
- **Published:** September 13, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=45381e91bfcc86d0

## About the Role

* 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

## Description

* 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


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