ML Engineer

Monarch; Emeryville (CA); Vor Ort; USD 140.000 - 210.000; Vollzeit

Benefits dieser Stelle

Equity

Zusammenfassung

Monarch in Emeryville, California is hiring a full-time software engineer to build reliable pipelines that carry data from assay recording to a reproducible model, evaluated predictions, and usable recommendations for the next experiment.

You will own ingestion, validation, versioning, and joining of assay videos and metadata, build scalable training and evaluation infrastructure, and collaborate with researchers to balance speed and scientific rigor.

Qualifikationen

• 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.

Aufgaben

• 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

Kenntnisse

Python programming

ML/data systems

Testing & observability

Video data handling

Research collaboration

Jobbeschreibung

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