ThetaRay provides AI-driven anti-financial crime technology used by global banks and fintechs to detect money laundering and financial crimes.

Our Israel office is a key R&D hub where engineering, data, and research teams work together to build scalable, production-ready systems that power ThetaRay’s platform.

We are looking for a Senior Algorithm Engineer to join our team in Israel and help build the next generation of AI-driven financial crime detection.

In this role, you will work at the intersection of software engineering, algorithms, and large-scale data processing, building scalable production systems that power mission-critical financial crime detection solutions. You will design efficient architectures, develop high-quality code, and turn data-driven and machine learning models into robust real-world applications.

Technology: Python, PySpark, Apache Spark, NumPy, scikit-learn, TensorFlow / PyTorch, ETL pipelines, distributed systems, large-scale data processing

What you’ll work on:

• Designing and developing scalable data processing pipelines and backend systems
• Writing efficient, high-quality, and maintainable code in Python and PySpark
• Optimizing large-scale data workflows and distributed computing processes
• Implementing and integrating machine learning solutions into production systems
• Translating analytical and algorithmic models into scalable applications
• Improving the performance, reliability, and scalability of data and ML pipelines
• Collaborating with data scientists, analysts, engineers, and R&D teams to bring solutions into production
• Conducting code reviews and promoting software engineering best practices

• 3+ years of experience in software development, algorithm engineering, or data engineering
• Expert-level Python programming skills
• Strong experience with PySpark / Apache Spark
• Experience working with large-scale distributed data processing
• Ability to write high-performance, optimized, and production-grade code
• Solid understanding of machine learning algorithms and concepts
• Experience implementing ML models in production environments
• Familiarity with supervised and unsupervised learning, feature engineering, and model evaluation and validation
• Experience with libraries such as NumPy, scikit-learn, TensorFlow, or PyTorch
• Strong understanding of data pipelines, ETL processes, and distributed systems
• Experience working with large and complex datasets
• Strong debugging, optimization, and performance tuning skills
• Strong analytical and problem-solving skills
• High attention to detail, code quality, and maintainability
• Strong communication and collaboration skills, with the ability to work independently and take ownership of technical solutions

Advantages

• Familiarity with cloud environments or big data platforms