Prisma-Photonics is a rapidly growing startup company, developing the next-generation smart-infrastructure solution based on novel fiber-sensing technology (smart roads, smart cities, perimeters, and grid monitoring, etc.). The company offers an award-winning disruptive solution; a “sensor free” approach to smart infrastructure. The company is VC backed and in the revenues stage.

Combining pioneering technology in optical fiber sensing with state-of-the-art machine learning, we help prevent environmental disasters, protect human lives, and keep critical energy and transportation backbones running smoothly.

We are seeking a motivated and curious Deep Learning Student Engineer to join our R&D team. This is a hands-on role for a student who wants to work with real-world data, explore deep learning techniques, and contribute to the ML infrastructure powering cutting-edge fiber-sensing technology.

• Support Data Tools & Pipelines – Help develop and maintain data libraries, preprocessing utilities, and pipelines for large-scale acoustic and spatiotemporal data.
• Experiment with ML & DL Models – Collaborate on the development, training, and evaluation of models for event detection, classification, and tracking.
• Analyze & Visualize Real-World Data – Explore diverse datasets, extract insights, and build visualizations that support research and engineering work.
• Collaborate Across Disciplines – Work with ML engineers, DL Researchers, physicists, and field teams to understand sensing challenges and contribute to ongoing projects.
• Write Clean, Maintainable Code – Implement tools, experiments, and data-processing modules in Python, following good practices with guidance from senior engineers.
• Learn and Grow – Stay updated on ML research, tools, and frameworks while gaining hands-on industry experience in a fast-paced environment.

• Currently pursuing a M.Sc. in Computer Science, Electrical Engineering, Physics, or a related field.
• Coursework or practical experience in machine learning or deep learning.
• Strong programming skills in Python.
• Familiarity with NumPy, Pandas, and at least one deep learning framework (e.g., PyTorch, TensorFlow).
• Ability to work independently, ask questions, and collaborate effectively.
• Analytical mindset and willingness to work with complex, real-world data.

• Experience with large-scale or time-series / spatiotemporal datasets.
• Hands-on experience with PyTorch or PyTorch Lightning.
• Familiarity with acoustic, seismic, or geospatial data.
• Experience with visualization libraries such as matplotlib or Plotly.
• Participation in ML research, academic projects, or open-source contributions.