About SirenOpt

At SirenOpt®, we are building the manufacturing intelligence layer for advanced materials. We help manufacturers visualize, understand, and control the material behavior that determines yield, performance, and safety across energy, aerospace, automotive, electronics, and semiconductor applications.

We are working on this problem because advanced materials are becoming more critical and more complex, but the tools used to manufacture them have not kept pace. Manufacturers still lack real-time visibility into the small variations that shape quality on the line. We care because those invisible variations cause scrap, waste energy and materials, create higher costs, inconsistent product performance, and introduce avoidable safety risks.

We developed PlasmaSens™, our manufacturing intelligence platform, to close that visibility gap and to lay the foundation for truly autonomous manufacturing.

We are uniquely suited to solve this problem because our expertise and our platform connect material science, plasma physics, machine learning, and manufacturing in one system. The same core technology works in-line, at-line, and off-line, and across form factors ranging from electrodes and wafers to 3D components. That flexibility lets us meet manufacturers where they are and carry insight from the lab into mass production.

We have deployed multiple PlasmaSens products with customers and collaborators across the United States, Japan, Germany, and Taiwan. We pair that real-world experience with support from leading US, European, and Japanese venture investors, the National Science Foundation, the California Energy Commission, and the Department of Energy. Our combination of differentiated technology, manufacturing experience, and global scientific and industry partnerships gives us the foundation to solve material variability at scale.

About the job

Job Title: Metrology Data Scientist – Signal Modeling & Applied Metrology

Location: On-Site (San Leandro, CA)

Job Type: Full-Time

Role Overview

We are seeking a Metrology Data Scientist to join our Applications Engineering team. In this role, you will build and deploy machine learning models that turn complex, high-dimensional sensor signals into actionable predictions about material properties, bridging the gap between raw instrument data and manufacturing intelligence.

This is a forward-deployed, customer-adjacent role. You will work directly with customer samples and datasets to execute proof-of-concept studies, validate model performance on novel materials, and translate results into product improvements. You will collaborate closely with software and hardware engineering teams to move models from research into production.

What You'll Do

Model Development & Calibration

Build, calibrate, and validate predictive models that map sensor signal features to material properties

Design and evaluate new model architectures and featurization strategies suited to small-data, high-dimensional scientific datasets

Apply methods including regression, dimensionality reduction, probabilistic modeling, anomaly detection, and physics-informed ML

Model Validation & Production Readiness

Develop testing and validation frameworks for model performance, including uncertainty quantification and out-of-distribution detection

Characterize model robustness across sample types, process conditions, and instrument configurations

Prepare models and documentation for handoff to the software engineering team for production deployment

Customer-Facing Proof-of-Concept Work

Analyze datasets from customer proof of concepts

Compile technical reports and supporting materials to deliver to customers

Translate findings and stakeholder feedback into model improvement roadmaps

What We're Looking For

B.S. in Data Science, Statistics, Applied Mathematics, or a related quantitative science field with 3-5 years of applied ML/data science experience; or M.S. with 1-3 years (Ph.D. a plus, not required)

Hands-on experience building and validating predictive models (supervised and self-supervised) in Python