Machine Learning Engineer

Company Description

Rapida is an AI-infused all-in-one Asset-Space® provider based in Haifa. Rapida enables infrastructure owners, inspectors, and civil engineers to efficiently acquire, analyze, and manage asset data, saving time and costs associated with maintenance.

Role Description

This is a full-time hybrid role for a Machine Learning Engineer at Rapida in Haifa, with flexibility for some remote work. The Engineer will be responsible for developing and implementing machine learning algorithms, conducting pattern recognition, and utilizing neural networks and statistical analysis to optimize asset management.

Key Responsibilities

• Design, implement, and train computer vision models using PyTorch.
• Develop and maintain data loading and pre-processing algorithms, primarily written in Rust.
• Optimize and fine-tune models for performance, accuracy, and efficiency.
• Integrate the resulting models into our services and data processing pipelines.
• Define, implement, and evaluate custom metrics for ML experiments to measure success.
• Collaborate with domain experts to gather insights, interpret industry standards, and design machine-learning solutions aimed at automating processes and reducing manual labor.

Required skills

• 3+ years of experience in machine learning.
• Solid understanding of neural networks, especially CNNs and transformer-based vision models.
• Experience with training and optimizing large-scale models on GPUs.
• Familiarity with data processing libraries such as NumPy, Pandas, and OpenCV.
• Strong problem-solving and troubleshooting skills, with the ability to debug and optimize complex systems.
• Experience writing software in Rust and/or C++.
•  A good understanding of linear algebra and Euclidian geometry.
• Ability to work independently and in a team environment
• Excellent problem-solving and analytical skills
• Master's or PhD in Computer Science, Engineering, or related field

Preferred skills

·      Experience writing software in Rust.

·      Familiarity with Kubernetes.