Job Description:
Low-Level Signal Processing – Transform raw millisecond-scale
waveforms into meaningful features: design signal processing
pipelines, extract spectral and temporal signatures of
wind-induced motion, and craft features that power the next
layers of modelling.
? Research, train, and optimize models that infer local wind speed
and direction, conductor temperature and strain, and detect
anomalous events—leveraging cutting-edge AI techniques to
explore a fascinating, largely untapped domain.
? Deliver physical and mathematical insights from the data; work
closely with academic partners to design preprocessing,
augmentation, and physics-context layers that translate
wind-induced vibrations into accurate wind metrics.
? Write, test, and maintain reliable code that operates 24/7 in
production and integrates seamlessly with utility systems.
? Shape the team’s data roadmap, mentor peers, and champion
best practices in MLOps, experimentation, and documentation.
Job Qualifications:
Advanced degree (M.Sc. or Ph.D.) in Electrical Engineering,
Physics, Applied Mathematics, Computer Science, or a related
quantitative discipline.
5+ years of hands-on experience in developing and deploying
machine learning, signal processing, or algorithmic solutions, with
emphasis on raw or low-level data (e.g., sensor data, audio,
video streams, or medical imaging).
? Proven expertise in time-series analysis and handling large-scale,
complex datasets from acquisition to production deployment.
? Strong Python programming skills, with the ability to write clean,
modular, and testable production-grade code.
Demonstrated experience deploying ML/DSP pipelines into
production environments, ideally in high-availability systems.
? Familiarity with ML/DL frameworks such as PyTorch, TensorFlow,
scikit-learn, and gradient-boosting libraries (e.g., XGBoost,
LightGBM).
? Strong collaboration and communication skills, with the ability to
work effectively across interdisciplinary teams including software
engineers, physicists, and external stakeholders.
Company Occupation:
High Tech
Company Size:
Small (0 - 50)