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At ThetaRay, our purpose is to make the world a safer place by protecting the integrity of the global financial system.
We do this by putting AI at the core of both our technology and our way of working. Our AI-driven solutions help banks and fintech companies worldwide detect and stop serious financial crime, from human trafficking and terrorist financing to sophisticated money laundering, while advanced technology, automation, and AI-driven tools help our teams collaborate smarter, move faster, and continuously improve how we build, deliver, and innovate.
We are looking for an Algorithm Researcher to turn expertise, initiative, and bold thinking into real impact on the next generation of AI-driven financial crime detection.
If you combine strong mathematical and research capabilities with advanced AI expertise, and if you are motivated by turning complex theoretical concepts into scalable, production-ready algorithms that solve real-world financial crime challenges, ThetaRay could be your next challenge.
What you'll do
Designing and developing advanced algorithms for complex financial crime detection challenges
Translating mathematical models and research concepts into scalable production systems
Building and optimizing ML and LLM-based solutions for real-world deployment
Working with transformers, attention mechanisms, sequence modeling, and representation learning
Developing solutions using RAG, embedding models, vector databases, and generative AI evaluation frameworks
Designing AI agents, tool-using LLM architectures, and autonomous decision-making pipelines
Improving model accuracy, robustness, explainability, and inference efficiency
Collaborating with engineers, data scientists, and domain experts to bring research into production
Requirements
MSc or PhD in Physics, Applied Mathematics, Computational Mathematics, or Statistics.
At least 3 years of experience in algorithm development, quantitative research, or advanced AI/ML roles.
Strong background in linear algebra and probability theory.
Strong background in stochastic processes and optimization.
Strong background in numerical methods and statistical modeling.
Deep understanding of modern deep learning architectures, including transformers, attention mechanisms, sequence modeling, and representation learning.
Hands-on experience with PyTorch or TensorFlow.
Experience building, fine-tuning, optimizing, or deploying large language models (LLMs).
Familiarity with RAG (retrieval-augmented generation), embedding models, and vector databases.
Familiarity with prompt engineering and evaluation frameworks for generative AI.
Expert-level Python skills, including NumPy, SciPy, and Pandas.
Strong understanding of algorithm design, complexity analysis, and data structures.
Experience with large-scale data processing.
Experience building AI-based systems in production.
Nice to have
Background in signal processing, dynamical systems, or computational physics (Advantage).
Experience with graph algorithms, anomaly detection, risk modeling, or information retrieval (Advantage).
Experience with model optimization, quantization, or distillation (Advantage).
Proven publication record in a relevant field (Advantage).