ML Engineer
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Before you apply
SalaryNo pay range posted
LocationEmeryville, California
Work modeon-site
Typefull-time
DepartmentR&D
EquityIncluded
Company size10+ people
PostedSep 5, 2026 · 2mo ago
Verified live2d ago
At a glanceSummarised by Seekless from the posting.
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
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
Skills
Python
PyTorch
JAX
TensorFlow
Google Cloud
Benefits
Equity
About the company
We offer opportunities to do your life’s work while helping solve one of the most important technical and moral challenges of our time.
About the role
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.
Responsibilities
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
Requirements
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
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