ML Engineer

mZero; Emeryville (CA); On-site; USD 160,000 - 260,000; Full time

Job summary

mZero in Emeryville, CA is looking for a Full-time, in-office ML systems engineer to build reliable data pipelines from assay recordings to models and actionable recommendations. You will own ingestion, validation, versioning, and joining of video, metadata, compound records, model features, and outcomes.

You will deploy training and evaluation pipelines, ensure data lineage and reproducible workflows, and work with researchers to surface ranked recommendations through mZero tools while

Qualifications

• Production Python software engineering experience.
• Cloud deployment and data pipeline experience.
• Ability to work with large video datasets and structured scientific data.
• Collaborate closely with researchers while making engineering tradeoffs.

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 data lineage, model versioning, automated tests, and auditable outputs.
• Turn research prototypes into dependable batch and online systems to rank compounds and surface recommendations.
• Monitor data quality, distribution shift, calibration, latency, cost, and failures as labs and assays grow.
• Design interfaces between computer vision, molecular models, active-learning systems, and the lab workflow.
• Improve developer velocity without weakening reproducibility or access controls.

Skills

Python

ML systems

Cloud deployments

Testing & observability

Education

Bachelor's degree or higher

Tools

PyTorch

TensorFlow

JAX

Job description

Employer: mZero.

Employment: Full-time. In-office in Emeryville, California. Reports directly to the CEO.

Annual base salary: $160,000–$260,000, plus equity.

Build the reliable systems that carry mZero 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 mZero 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

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 workflowsAbility 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