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ML Engineer
Career Insights for Machine Learning Engineer
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Based on California data
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$168,439 / year median in California
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 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