DataML engineer, fusion data

Fusionality is building the measurement simulation and control stack for the fusion energy reactors of the future.

Spun out of EPFLs 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.

• Build maintain and run pipelines that bring experimental fusion data into high quality consistent documented versioned datasets.

• Develop ML-assisted labelling and event detection (e.g. disruptions MHD activity diagnostic faults) using weak supervision active learning and anomaly detection.

• Own data quality and provisioning: provenance calibration metadata automated validation. Manage and facilitate access for the rest of the team.

• 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.

• Hands-on experience building data pipelines for large scientific or industrial time-series data formats (e.g. 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 labelling.

• Solid software engineering habits: testing reproducibility data versioning are second nature to you.

• Evidence of independent work at the highest level: a PhD thesis and/or published code that is used by others or an industry project you owned. You should be comfortable designing data architectures independently from day 1.

• The following are an advantage but not strictly required:

• Human-in-the-loop labelling tools and workflows.

• Pipeline Orchestration (e.g. Dagster Airflow Prefect) and data versioning (e.g. DVC lakeFS).

• Data from large physics experiments: fusion accelerators astronomy or exposure to fusion data systems (MDSplus IMAS).

• Play a key role at a dynamic fast-growing startup shaping the technical solutions and products from the ground up.

• Build cutting edge technology to monitor and control plasmas in real fusion devices working side-by-side with leading plasma and control experts.

• Great working conditions located in Lausanne Switzerland a city and country known for its high quality of life.

• Competitive salary and equity compensation matched to level of seniority and benefits including immigration assistance.