ML Engineer

The role

Build reliable systems to carry data from assay recording to reproducible models and usable recommendations. Own pipelines for ingesting, validating, and joining assay videos, metadata, and experimental outcomes.

What you will do

• 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
• Monitor data quality, distribution shift, calibration, latency, cost, and failures
• 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

Required 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

Nice to have

• 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

Benefits

• Compensation includes equity

About the company

monarchcrops offers opportunities to do your life’s work while helping solve technical and moral challenges.