Description
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
Requirements
- 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
