Job description

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 Ability to analyze multivariate, high-dimensional datasets and perform feature engineering and selection Solid grasp of statistical modeling: uncertainty quantification, regularization, covariate analysis, and feature importance methods Strong communicator; comfortable presenting technical findings to both technical and non-technical audiences Nice to have: Experience working with time-series, spectroscopic, or other sensor-based signal data Prior work in manufacturing, materials science, energy storage, semiconductors, or another physical science domain Prior customer-facing or applications engineering experience in a technical product company Experience deploying models in production software environments Familiarity with data pipeline development (PostgreSQL or similar) Fluency in Mandarin Chinese, Japanese, German, Korean, or another key stakeholder language Base Pay Range $120,000 — $170,000 USD Benefits Equity and Salary compensation depends on experience Health, Dental, Vision plans provided 401k matching provided Time off: 20 days of PTO per year, plus approximately 15 paid US holidays per year

Requirements

Must-haves

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
Hands-on experience building and validating predictive models (supervised and self-supervised) in Python
Ability to analyze multivariate, high-dimensional datasets and perform feature engineering and selection

Nice-to-haves

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)
Experience working with time-series, spectroscopic, or other sensor-based signal data
Prior work in manufacturing, materials science, energy storage, semiconductors, or another physical science domain
Prior customer-facing or applications engineering experience in a technical product company
Experience deploying models in production software environments
Familiarity with data pipeline development (PostgreSQL or similar)
Fluency in Mandarin Chinese, Japanese, German, Korean, or another key stakeholder language
Equity and Salary compensation depends on experience
Health, Dental, Vision plans provided
Time off: 20 days of PTO per year, plus approximately 15 paid US holidays per year

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlpython

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Pto