Senior deep learning researcher

We are a fast growing start up, transforms infrastructure monitoring by leveraging optical fibers to monitor critical, large-scale systems like power grids and oil & gas pipelines across thousands of kilometers without the need for traditional sensors. Our Hyper-Scan Fiber-Sensing technology, combined with advanced Machine Learning, empowers utility operators to achieve environmental and renewable energy goals while maintaining operational excellence on their path to net-zero emissions. Founded in 2017, with offices in the U.S., Europe, and Israel (HQ), we are led by an expert team with a proven track record in building and scaling successful companies within the lasers and deep-tech sectors. We are seeking a talented, self-driven and passionate Deep Learning Engineer who will bring both deep learning expertise and engineering skills to our unique domain of Optical Fiber Sensing. In this role, you will balance cutting-edge ML research with the practical, large-scale data handling challenges that enable our solutions to thrive in the real world. About half of your day-to-day work will involve building and optimizing the data processing infrastructure and libraries that underpin our ML models.
What Youll Do:
* Design and implement custom data libraries and formats that support fast random access to massive Real-Time data streams. Work closely with the engineering team to ensure data pipelines are robust, scalable, and efficient.
* Leverage and extend data processing frameworks (e.g., Pandas, GeoPandas, Dask) to handle large-scale acoustic data. Write or customize Pandas extensions to meet specialized data modeling requirements.
* Spearhead core. deep learning initiatives to solve real-world challenges in detecting, classifying, and tracking spatiotemporal events. Push boundaries by inventing and applying novel ML and DL techniques tailored to our unique optical fiber domain
* Deliver well-tested, reliable, and maintainable code that seamlessly integrates into our rich and unique code infrastructure and ecosystem.
* Work alongside physicists, hardware engineers, data scientists, and field teams to gather requirements, plan real-world experiments, and collect critical data. Translate complex domain challenges into effective ML and data -engineering solutions.
* Harness advanced data analysis and visualization tools to uncover insights from diverse and voluminous real-world datasets.
* Continuously research new algorithms, technologies, and tools (both ML- and data -engineeringrelated) to keep us at the forefront of innovation.
* Rapid prototyping, iterative experimentation, and fast-paced project turnovers are the norms. Youll balance quick delivery with rigor and thoroughness.