Fusionality is building the measurement, simulation, and control stack for the fusion energy reactors of the future.
Spun out of EPFL’s Swiss Plasma Center and backed by top investors, our technology enables reliable operation, real-time monitoring, and analysis of high-temperature plasmas in fusion devices.
We are founded by world-class experts at the intersection of fusion physics, advanced control, and AI (ex-Google DeepMind, EPFL).
We are assembling a founding engineering team in Lausanne to build our core technology platform.
We are looking for a Data / Machine Learning Engineer to join our team.
This job is for you if you like turning large, messy, heterogeneous experimental data into clean, labelled, trustworthy datasets.
You have done this with scientific or industrial sensor data.
Familiarity with fusion is a plus but not a strict requirement.
ResponsibilitiesBuild, maintain and run pipelines that bring experimental fusion data into high quality, consistent, documented, versioned datasets.
disruptions, MHD activity, diagnostic faults), using weak supervision, active learning and anomaly detection.
Work with physicists validating the datasets & labels.
Work with our computational scientists and control engineers, who build models and controllers on these datasets, and with customers to understand and integrate their data.
RequirementsHands-on experience building data pipelines for large scientific or industrial time-series data formats (e.
HDF5, Parquet, Zarr).
Proficiency in designing, querying and optimizing SQL and noSQL database systems and familiarity with Time Series databases such as TimescaleDB, InfluxDB.
Experience in applying ML techniques to time series in Python (PyTorch or JAX): sequence models, anomaly and event detection, semi- or weakly-supervised l.