Deep Learning Student Position

Description:

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.

What You’ll Do:
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.
What You Bring:
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.

Great to Have:
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.