About this role
Our team is new, growing, and evolving quickly, and we are hiring across multiple disciplines. We work at the intersection of machine learning, hardware, and software, creating technologies that reshape how people communicate and experience the world around them in everyday life.
We are looking for a scientist who can turn experimental results into deep physical insight. Working closely with experimental teams gathering raw data, you will analyze multi-source datasets to uncover the connections between signals, derived features, and system-level performance metrics. You will combine data-driven modeling with first-principles physical intuition to explain performance dynamics and isolate the root causes driving them.
Minimum Qualifications
MSc or PhD in physics, electrical engineering, or a related quantitative field.
3+ years of relevant industry experience.
Strong signal processing background (time-frequency analysis, noise, coherence, filtering).
Experience with large-scale data analysis
Strong understanding of experimental physics and measurement systems, enough to interpret results and spot artifacts.
Solid statistics: hypothesis testing, regression, uncertainty estimation, and feature selection.
Ability to work independently on open-ended problems.
Preferred Qualifications
Machine learning frameworks (scikit-learn, PyTorch) and model interpretability techniques.
Experience with optical, photonic, or sensor data.
Distributed or high-volume data processing.
Occasional hands-on lab experience.