Experienced Software Engineer - Data Infrastructure

We are looking for a Software Engineer to join our Object Detection group and play a key role in managing and enabling data workflows that power our computer vision algorithms.

Our team generates and processes large volumes of diverse data from multiple sources, and we need someone who can bridge the gap between raw data and algorithmic innovation. Working alongside algorithm developers and researchers, you'll be responsible for building robust data pipelines, ensuring data quality, and making complex datasets accessible and actionable for our detection models.

This role requires both strong coding abilities and a deep understanding of data - you'll need to think like an engineer while understanding the unique needs of a research and development-focused algorithmic group. A significant portion of the work is highly independent, meaning you will be expected to drive projects end-to-end, taking full ownership from understanding the initial problem and modeling the solution to executing and delivering it yourself.

What will your job look like:

• Design, build, and write the underlying code for large-scale systems handling diverse data from multiple sources
• Build and maintain robust data pipelines that enable algorithmic development
• Evaluate various optional technologies and take part in decision-making
• Own key features and services within the data infrastructure
• Ensure data quality and accessibility for detection models and research needs

All you need is:

• BSc. in Computer Science or equivalent - must
• 2+ years of software development experience
• Proven ability to write high-quality, production-grade Python code (understanding of OOP, modularity, and system design)
• Deep understanding of complex data structures, massive ETL processes, and data workflows
• Experience with databases (SQL/NoSQL)

Advantages:

• Experience with modern Data Lakehouse architectures and table formats (e.g., Apache Iceberg, Parquet)
• A solid understanding of Machine Learning (ML) concepts and principles
• Experience integrating and utilizing ML models within data workflows and production pipelines
• Familiarity with MLOps practices or working alongside algorithm and research teams